Showing 1–50 of 118 results for author: De Cristofaro, E

PROTEAN: Federated Intrusion Detection in Non-IID Environments through Prototype-Based Knowledge Sharing

Authors: Sara Chennoufi, Yufei Han, Gregory Blanc, Emiliano De Cristofaro, Christophe Kiennert

Abstract: In distributed networks, participants often face diverse and fast-evolving cyberattacks. This makes techniques based on Federated Learning (FL) a promising mitigation strategy. By only exchanging model updates, FL participants can collaboratively build detection models without revealing sensitive information, e.g., network structures or security postures. However, the effectiveness of FL solutions… ▽ More In distributed networks, participants often face diverse and fast-evolving cyberattacks. This makes techniques based on Federated Learning (FL) a promising mitigation strategy. By only exchanging model updates, FL participants can collaboratively build detection models without revealing sensitive information, e.g., network structures or security postures. However, the effectiveness of FL solutions is often hindered by significant data heterogeneity, as attack patterns often differ drastically across organizations due to varying security policies. To address these challenges, we introduce PROTEAN, a Prototype Learning-based framework geared to facilitate collaborative and privacy-preserving intrusion detection. PROTEAN enables accurate detection in environments with highly non-IID attack distributions and promotes direct knowledge sharing by exchanging class prototypes of different attack types among participants. This allows organizations to better understand attack techniques not present in their data collections. We instantiate PROTEAN on two cyber intrusion datasets collected from IIoT and 5G-connected participants and evaluate its performance in terms of utility and privacy, demonstrating its effectiveness in addressing data heterogeneity while improving cyber attack understanding in federated intrusion detection systems (IDSs). △ Less

Submitted 7 July, 2025; originally announced July 2025.

Journal ref: Published in the Proceedings of the 30th European Symposium on Research in Computer Security (ESORICS 2025)

The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing

Authors: Meenatchi Sundaram Muthu Selva Annamalai, Borja Balle, Jamie Hayes, Georgios Kaissis, Emiliano De Cristofaro

Abstract: This paper systematizes research on auditing Differential Privacy (DP) techniques, aiming to identify key insights into the current state of the art and open challenges. First, we introduce a comprehensive framework for reviewing work in the field and establish three cross-contextual desiderata that DP audits should target--namely, efficiency, end-to-end-ness, and tightness. Then, we systematize t… ▽ More This paper systematizes research on auditing Differential Privacy (DP) techniques, aiming to identify key insights into the current state of the art and open challenges. First, we introduce a comprehensive framework for reviewing work in the field and establish three cross-contextual desiderata that DP audits should target--namely, efficiency, end-to-end-ness, and tightness. Then, we systematize the modes of operation of state-of-the-art DP auditing techniques, including threat models, attacks, and evaluation functions. This allows us to highlight key details overlooked by prior work, analyze the limiting factors to achieving the three desiderata, and identify open research problems. Overall, our work provides a reusable and systematic methodology geared to assess progress in the field and identify friction points and future directions for our community to focus on. △ Less

Submitted 30 June, 2025; v1 submitted 19 June, 2025; originally announced June 2025.

The Ethics of Generative AI in Anonymous Spaces: A Case Study of 4chan's /pol/ Board

Authors: Parth Gaba, Emiliano De Cristofaro

Abstract: This paper presents a characterization of AI-generated images shared on 4chan, examining how this anonymous online community is (mis-)using generative image technologies. Through a methodical data collection process, we gathered 900 images from 4chan's /pol/ (Politically Incorrect) board, which included the label "/mwg/" (memetic warfare general), between April and July 2024, identifying 66 unique… ▽ More This paper presents a characterization of AI-generated images shared on 4chan, examining how this anonymous online community is (mis-)using generative image technologies. Through a methodical data collection process, we gathered 900 images from 4chan's /pol/ (Politically Incorrect) board, which included the label "/mwg/" (memetic warfare general), between April and July 2024, identifying 66 unique AI-generated images. The analysis reveals concerning patterns in the use of this technology, with 69.7% of images including recognizable figures, 28.8% of images containing racist elements, 28.8% featuring anti-Semitic content, and 9.1% incorporating Nazi-related imagery. Overall, we document how users are weaponizing generative AI to create extremist content, political commentary, and memes that often bypass conventional content moderation systems. This research highlights significant implications for platform governance, AI safety mechanisms, and broader societal impacts as generative AI technologies become increasingly accessible. The findings underscore the urgent need for enhanced safeguards in generative AI systems and more effective regulatory frameworks to mitigate potential harms while preserving innovation. △ Less

Submitted 17 June, 2025; originally announced June 2025.

Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy

Authors: Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Sofiane Mahiou, Emiliano De Cristofaro

Abstract: Privacy attacks, particularly membership inference attacks (MIAs), are widely used to assess the privacy of generative models for tabular synthetic data, including those with Differential Privacy (DP) guarantees. These attacks often exploit outliers, which are especially vulnerable due to their position at the boundaries of the data domain (e.g., at the minimum and maximum values). However, the ro… ▽ More Privacy attacks, particularly membership inference attacks (MIAs), are widely used to assess the privacy of generative models for tabular synthetic data, including those with Differential Privacy (DP) guarantees. These attacks often exploit outliers, which are especially vulnerable due to their position at the boundaries of the data domain (e.g., at the minimum and maximum values). However, the role of data domain extraction in generative models and its impact on privacy attacks have been overlooked. In this paper, we examine three strategies for defining the data domain: assuming it is externally provided (ideally from public data), extracting it directly from the input data, and extracting it with DP mechanisms. While common in popular implementations and libraries, we show that the second approach breaks end-to-end DP guarantees and leaves models vulnerable. While using a provided domain (if representative) is preferable, extracting it with DP can also defend against popular MIAs, even at high privacy budgets. △ Less

Submitted 13 April, 2025; v1 submitted 11 April, 2025; originally announced April 2025.

Comments: Accepted to the Synthetic Data x Data Access Problem workshop (SynthData), part of ICLR 2025

The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data

Authors: Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Sofiane Mahiou, Emiliano De Cristofaro

Abstract: Differentially Private (DP) generative marginal models are often used in the wild to release synthetic tabular datasets in lieu of sensitive data while providing formal privacy guarantees. These models approximate low-dimensional marginals or query workloads; crucially, they require the training data to be pre-discretized, i.e., continuous values need to first be partitioned into bins. However, as… ▽ More Differentially Private (DP) generative marginal models are often used in the wild to release synthetic tabular datasets in lieu of sensitive data while providing formal privacy guarantees. These models approximate low-dimensional marginals or query workloads; crucially, they require the training data to be pre-discretized, i.e., continuous values need to first be partitioned into bins. However, as the range of values (or their domain) is often inferred directly from the training data, with the number of bins and bin edges typically defined arbitrarily, this approach can ultimately break end-to-end DP guarantees and may not always yield optimal utility. In this paper, we present an extensive measurement study of four discretization strategies in the context of DP marginal generative models. More precisely, we design DP versions of three discretizers (uniform, quantile, and k-means) and reimplement the PrivTree algorithm. We find that optimizing both the choice of discretizer and bin count can improve utility, on average, by almost 30% across six DP marginal models, compared to the default strategy and number of bins, with PrivTree being the best-performing discretizer in the majority of cases. We demonstrate that, while DP generative models with non-private discretization remain vulnerable to membership inference attacks, applying DP during discretization effectively mitigates this risk. Finally, we propose an optimized approach for automatically selecting the optimal number of bins, achieving high utility while reducing both privacy budget consumption and computational overhead. △ Less

Submitted 13 April, 2025; v1 submitted 9 April, 2025; originally announced April 2025.

VIKI: Systematic Cross-Platform Profile Inference of Online Users

Authors: Ben Treves, Emiliano De Cristofaro, Yue Dong, Michalis Faloutsos

Abstract: What can we learn about online users by comparing their profiles across different platforms? We use the term profile to represent displayed personality traits, interests, and behavioral patterns (e.g., offensiveness). We also use the term {\it displayed personas} to refer to the personas that users manifest on a platform. Though individuals have a single real persona, it is not difficult to imagin… ▽ More What can we learn about online users by comparing their profiles across different platforms? We use the term profile to represent displayed personality traits, interests, and behavioral patterns (e.g., offensiveness). We also use the term {\it displayed personas} to refer to the personas that users manifest on a platform. Though individuals have a single real persona, it is not difficult to imagine that people can behave differently in different ``contexts'' as it happens in real life (e.g., behavior in office, bar, football game). The vast majority of previous studies have focused on profiling users on a single platform. Here, we propose VIKI, a systematic methodology for extracting and integrating the displayed personas of users across different social platforms. First, we extract multiple types of information, including displayed personality traits, interests, and offensiveness. Second, we evaluate, combine, and introduce methods to summarize and visualize cross-platform profiles. Finally, we evaluate VIKI on a dataset that spans three platforms -- GitHub, LinkedIn, and X. Our experiments show that displayed personas change significantly across platforms, with over 78% of users exhibiting a significant change. For instance, we find that neuroticism exhibits the largest absolute change. We also identify significant correlations between offensive behavior and displayed personality traits. Overall, we consider VIKI as an essential building block for systematic and nuanced profiling of users across platforms. △ Less

Submitted 18 March, 2025; originally announced March 2025.

Comments: Published in the Proceedings of the 17th ACM Web Science Conference (WebSci 2025). Please cite the WebSci version

Beyond the Crawl: Unmasking Browser Fingerprinting in Real User Interactions

Authors: Meenatchi Sundaram Muthu Selva Annamalai, Igor Bilogrevic, Emiliano De Cristofaro

Abstract: Browser fingerprinting is a pervasive online tracking technique used increasingly often for profiling and targeted advertising. Prior research on the prevalence of fingerprinting heavily relied on automated web crawls, which inherently struggle to replicate the nuances of human-computer interactions. This raises concerns about the accuracy of current understandings of real-world fingerprinting dep… ▽ More Browser fingerprinting is a pervasive online tracking technique used increasingly often for profiling and targeted advertising. Prior research on the prevalence of fingerprinting heavily relied on automated web crawls, which inherently struggle to replicate the nuances of human-computer interactions. This raises concerns about the accuracy of current understandings of real-world fingerprinting deployments. As a result, this paper presents a user study involving 30 participants over 10 weeks, capturing telemetry data from real browsing sessions across 3,000 top-ranked websites. Our evaluation reveals that automated crawls miss almost half (45%) of the fingerprinting websites encountered by real users. This discrepancy mainly stems from the crawlers' inability to access authentication-protected pages, circumvent bot detection, and trigger fingerprinting scripts activated by specific user interactions. We also identify potential new fingerprinting vectors present in real user data but absent from automated crawls. Finally, we evaluate the effectiveness of federated learning for training browser fingerprinting detection models on real user data, yielding improved performance than models trained solely on automated crawl data. △ Less

Submitted 3 February, 2025; originally announced February 2025.

Comments: A slightly shorter version of this paper appears in the Proceedings of the 34th "The Web Conference'' (WWW 2025). Please cite the WWW version

To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling

Authors: Meenatchi Sundaram Muthu Selva Annamalai, Borja Balle, Jamie Hayes, Emiliano De Cristofaro

Abstract: The Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm allows the training of machine learning (ML) models with formal Differential Privacy (DP) guarantees. Since DP-SGD processes training data in batches, it employs Poisson sub-sampling to select each batch at every step. However, it has become common practice to replace sub-sampling with shuffling owing to better compatibility… ▽ More The Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm allows the training of machine learning (ML) models with formal Differential Privacy (DP) guarantees. Since DP-SGD processes training data in batches, it employs Poisson sub-sampling to select each batch at every step. However, it has become common practice to replace sub-sampling with shuffling owing to better compatibility and computational overhead. At the same time, we do not know how to compute tight theoretical guarantees for shuffling; thus, DP guarantees of models privately trained with shuffling are often reported as though Poisson sub-sampling was used. This prompts the need to verify whether gaps exist between the theoretical DP guarantees reported by state-of-the-art models and their actual leakage. To do so, we introduce a novel DP auditing procedure to analyze DP-SGD with shuffling and show that DP models trained with this approach have considerably overestimated privacy guarantees (up to 4 times). In the process, we assess the impact on privacy leakage of several parameters, including batch size, privacy budget, and threat model. Finally, we study two common variations of the shuffling procedure that result in even further privacy leakage (up to 10 times). Overall, our work attests to the risk of using shuffling instead of Poisson sub-sampling vis-à-vis privacy leakage from DP-SGD. △ Less

Submitted 12 April, 2025; v1 submitted 15 November, 2024; originally announced November 2024.

Revealing The Secret Power: How Algorithms Can Influence Content Visibility on Social Media

Authors: Alessandro Galeazzi, Pujan Paudel, Mauro Conti, Emiliano De Cristofaro, Gianluca Stringhini

Abstract: In recent years, the opaque design and the limited public understanding of social networks' recommendation algorithms have raised concerns about potential manipulation of information exposure. While reducing content visibility, aka shadow banning, may help limit harmful content, it can also be used to suppress dissenting voices. This prompts the need for greater transparency and a better understan… ▽ More In recent years, the opaque design and the limited public understanding of social networks' recommendation algorithms have raised concerns about potential manipulation of information exposure. While reducing content visibility, aka shadow banning, may help limit harmful content, it can also be used to suppress dissenting voices. This prompts the need for greater transparency and a better understanding of this practice. In this paper, we investigate the presence of visibility alterations through a large-scale quantitative analysis of two Twitter/X datasets comprising over 40 million tweets from more than 9 million users, focused on discussions surrounding the Ukraine-Russia conflict and the 2024 US Presidential Elections. We use view counts to detect patterns of reduced or inflated visibility and examine how these correlate with user opinions, social roles, and narrative framings. Our analysis shows that the algorithm systematically penalizes tweets containing links to external resources, reducing their visibility by up to a factor of eight, regardless of the ideological stance or source reliability. Rather, content visibility may be penalized or favored depending on the specific accounts producing it, as observed when comparing tweets from the Kyiv Independent and RT.com or tweets by Donald Trump and Kamala Harris. Overall, our work highlights the importance of transparency in content moderation and recommendation systems in protecting the integrity of public discourse and ensuring equitable access to online platforms. △ Less

Submitted 24 April, 2025; v1 submitted 22 October, 2024; originally announced October 2024.

The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging

Authors: Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro

Abstract: Synthetic data created by differentially private (DP) generative models is increasingly used in real-world settings. In this context, PATE-GAN has emerged as one of the most popular algorithms, combining Generative Adversarial Networks (GANs) with the private training approach of PATE (Private Aggregation of Teacher Ensembles). In this paper, we set out to reproduce the utility evaluation from t… ▽ More Synthetic data created by differentially private (DP) generative models is increasingly used in real-world settings. In this context, PATE-GAN has emerged as one of the most popular algorithms, combining Generative Adversarial Networks (GANs) with the private training approach of PATE (Private Aggregation of Teacher Ensembles). In this paper, we set out to reproduce the utility evaluation from the original PATE-GAN paper, compare available implementations, and conduct a privacy audit. More precisely, we analyze and benchmark six open-source PATE-GAN implementations, including three by (a subset of) the original authors. First, we shed light on architecture deviations and empirically demonstrate that none reproduce the utility performance reported in the original paper. We then present an in-depth privacy evaluation, which includes DP auditing, and show that all implementations leak more privacy than intended. Furthermore, we uncover 19 privacy violations and 5 other bugs in these six open-source implementations. Lastly, our codebase is available from: https://github.com/spalabucr/pategan-audit. △ Less

Submitted 10 February, 2025; v1 submitted 20 June, 2024; originally announced June 2024.

Comments: Published in Transactions on Machine Learning Research (TMLR 2025). Please cite the TMLR version

A Systematic Review of Federated Generative Models

Authors: Ashkan Vedadi Gargary, Emiliano De Cristofaro

Abstract: Federated Learning (FL) has emerged as a solution for distributed systems that allow clients to train models on their data and only share models instead of local data. Generative Models are designed to learn the distribution of a dataset and generate new data samples that are similar to the original data. Many prior works have tried proposing Federated Generative Models. Using Federated Learning a… ▽ More Federated Learning (FL) has emerged as a solution for distributed systems that allow clients to train models on their data and only share models instead of local data. Generative Models are designed to learn the distribution of a dataset and generate new data samples that are similar to the original data. Many prior works have tried proposing Federated Generative Models. Using Federated Learning and Generative Models together can be susceptible to attacks, and designing the optimal architecture remains challenging. This survey covers the growing interest in the intersection of FL and Generative Models by comprehensively reviewing research conducted from 2019 to 2024. We systematically compare nearly 100 papers, focusing on their FL and Generative Model methods and privacy considerations. To make this field more accessible to newcomers, we highlight the state-of-the-art advancements and identify unresolved challenges, offering insights for future research in this evolving field. △ Less

Submitted 26 May, 2024; originally announced May 2024.

Comments: 24 Pages, 3 Figures, 5 Tables

Nearly Tight Black-Box Auditing of Differentially Private Machine Learning

Authors: Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro

Abstract: This paper presents an auditing procedure for the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box threat model that is substantially tighter than prior work. The main intuition is to craft worst-case initial model parameters, as DP-SGD's privacy analysis is agnostic to the choice of the initial model parameters. For models trained on MNIST and CIFAR-10 at the… ▽ More This paper presents an auditing procedure for the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box threat model that is substantially tighter than prior work. The main intuition is to craft worst-case initial model parameters, as DP-SGD's privacy analysis is agnostic to the choice of the initial model parameters. For models trained on MNIST and CIFAR-10 at theoretical $\varepsilon=10.0$, our auditing procedure yields empirical estimates of $\varepsilon_{emp} = 7.21$ and $6.95$, respectively, on a 1,000-record sample and $\varepsilon_{emp}= 6.48$ and $4.96$ on the full datasets. By contrast, previous audits were only (relatively) tight in stronger white-box models, where the adversary can access the model's inner parameters and insert arbitrary gradients. Overall, our auditing procedure can offer valuable insight into how the privacy analysis of DP-SGD could be improved and detect bugs and DP violations in real-world implementations. The source code needed to reproduce our experiments is available at https://github.com/spalabucr/bb-audit-dpsgd. △ Less

Submitted 1 November, 2024; v1 submitted 22 May, 2024; originally announced May 2024.

Comments: To appear in the Proceedings of the Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024). Please cite accordingly

"What do you want from theory alone?" Experimenting with Tight Auditing of Differentially Private Synthetic Data Generation

Authors: Meenatchi Sundaram Muthu Selva Annamalai, Georgi Ganev, Emiliano De Cristofaro

Abstract: Differentially private synthetic data generation (DP-SDG) algorithms are used to release datasets that are structurally and statistically similar to sensitive data while providing formal bounds on the information they leak. However, bugs in algorithms and implementations may cause the actual information leakage to be higher. This prompts the need to verify whether the theoretical guarantees of sta… ▽ More Differentially private synthetic data generation (DP-SDG) algorithms are used to release datasets that are structurally and statistically similar to sensitive data while providing formal bounds on the information they leak. However, bugs in algorithms and implementations may cause the actual information leakage to be higher. This prompts the need to verify whether the theoretical guarantees of state-of-the-art DP-SDG implementations also hold in practice. We do so via a rigorous auditing process: we compute the information leakage via an adversary playing a distinguishing game and running membership inference attacks (MIAs). If the leakage observed empirically is higher than the theoretical bounds, we identify a DP violation; if it is non-negligibly lower, the audit is loose. We audit six DP-SDG implementations using different datasets and threat models and find that black-box MIAs commonly used against DP-SDGs are severely limited in power, yielding remarkably loose empirical privacy estimates. We then consider MIAs in stronger threat models, i.e., passive and active white-box, using both existing and newly proposed attacks. Overall, we find that, currently, we do not only need white-box MIAs but also worst-case datasets to tightly estimate the privacy leakage from DP-SDGs. Finally, we show that our automated auditing procedure finds both known DP violations (in 4 out of the 6 implementations) as well as a new one in the DPWGAN implementation that was successfully submitted to the NIST DP Synthetic Data Challenge. The source code needed to reproduce our experiments is available from https://github.com/spalabucr/synth-audit. △ Less

Submitted 16 May, 2024; originally announced May 2024.

Comments: To appear at Usenix Security 2024

iDRAMA-Scored-2024: A Dataset of the Scored Social Media Platform from 2020 to 2023

Authors: Jay Patel, Pujan Paudel, Emiliano De Cristofaro, Gianluca Stringhini, Jeremy Blackburn

Abstract: Online web communities often face bans for violating platform policies, encouraging their migration to alternative platforms. This migration, however, can result in increased toxicity and unforeseen consequences on the new platform. In recent years, researchers have collected data from many alternative platforms, indicating coordinated efforts leading to offline events, conspiracy movements, hate… ▽ More Online web communities often face bans for violating platform policies, encouraging their migration to alternative platforms. This migration, however, can result in increased toxicity and unforeseen consequences on the new platform. In recent years, researchers have collected data from many alternative platforms, indicating coordinated efforts leading to offline events, conspiracy movements, hate speech propagation, and harassment. Thus, it becomes crucial to characterize and understand these alternative platforms. To advance research in this direction, we collect and release a large-scale dataset from Scored -- an alternative Reddit platform that sheltered banned fringe communities, for example, c/TheDonald (a prominent right-wing community) and c/GreatAwakening (a conspiratorial community). Over four years, we collected approximately 57M posts from Scored, with at least 58 communities identified as migrating from Reddit and over 950 communities created since the platform's inception. Furthermore, we provide sentence embeddings of all posts in our dataset, generated through a state-of-the-art model, to further advance the field in characterizing the discussions within these communities. We aim to provide these resources to facilitate their investigations without the need for extensive data collection and processing efforts. △ Less

Submitted 16 May, 2024; originally announced May 2024.

"Here's Your Evidence": False Consensus in Public Twitter Discussions of COVID-19 Science

Authors: Alexandros Efstratiou, Marina Efstratiou, Satrio Yudhoatmojo, Jeremy Blackburn, Emiliano De Cristofaro

Abstract: The COVID-19 pandemic brought about an extraordinary rate of scientific papers on the topic that were discussed among the general public, although often in biased or misinformed ways. In this paper, we present a mixed-methods analysis aimed at examining whether public discussions were commensurate with the scientific consensus on several COVID-19 issues. We estimate scientific consensus based on s… ▽ More The COVID-19 pandemic brought about an extraordinary rate of scientific papers on the topic that were discussed among the general public, although often in biased or misinformed ways. In this paper, we present a mixed-methods analysis aimed at examining whether public discussions were commensurate with the scientific consensus on several COVID-19 issues. We estimate scientific consensus based on samples of abstracts from preprint servers and compare against the volume of public discussions on Twitter mentioning these papers. We find that anti-consensus posts and users, though overall less numerous than pro-consensus ones, are vastly over-represented on Twitter, thus producing a false consensus effect. This transpires with favorable papers being disproportionately amplified, along with an influx of new anti-consensus user sign-ups. Finally, our content analysis highlights that anti-consensus users misrepresent scientific findings or question scientists' integrity in their efforts to substantiate their claims. △ Less

Submitted 7 June, 2024; v1 submitted 24 January, 2024; originally announced January 2024.

Comments: Accepted for publication at 27th ACM Conference on Computer Supported Cooperative Work and Social Computing (ACM CSCW 2024). Please cite accordingly

From HODL to MOON: Understanding Community Evolution, Emotional Dynamics, and Price Interplay in the Cryptocurrency Ecosystem

Authors: Kostantinos Papadamou, Jay Patel, Jeremy Blackburn, Philipp Jovanovic, Emiliano De Cristofaro

Abstract: This paper presents a large-scale analysis of the cryptocurrency community on Reddit, shedding light on the intricate relationship between the evolution of their activity, emotional dynamics, and price movements. We analyze over 130M posts on 122 cryptocurrency-related subreddits using temporal analysis, statistical modeling, and emotion detection. While /r/CryptoCurrency and /r/dogecoin are the m… ▽ More This paper presents a large-scale analysis of the cryptocurrency community on Reddit, shedding light on the intricate relationship between the evolution of their activity, emotional dynamics, and price movements. We analyze over 130M posts on 122 cryptocurrency-related subreddits using temporal analysis, statistical modeling, and emotion detection. While /r/CryptoCurrency and /r/dogecoin are the most active subreddits, we find an overall surge in cryptocurrency-related activity in 2021, followed by a sharp decline. We also uncover a strong relationship in terms of cross-correlation between online activity and the price of various coins, with the changes in the number of posts mostly leading the price changes. Backtesting analysis shows that a straightforward strategy based on the cross-correlation where one buys/sells a coin if the daily number of posts about it is greater/less than the previous would have led to a 3x return on investment. Finally, we shed light on the emotional dynamics of the cryptocurrency communities, finding that joy becomes a prominent indicator during upward market performance, while a decline in the market manifests an increase in anger. △ Less

Submitted 12 December, 2023; originally announced December 2023.

The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets

Authors: Georgi Ganev, Emiliano De Cristofaro

Abstract: Generative models producing synthetic data are meant to provide a privacy-friendly approach to releasing data. However, their privacy guarantees are only considered robust when models satisfy Differential Privacy (DP). Alas, this is not a ubiquitous standard, as many leading companies (and, in fact, research papers) use ad-hoc privacy metrics based on testing the statistical similarity between syn… ▽ More Generative models producing synthetic data are meant to provide a privacy-friendly approach to releasing data. However, their privacy guarantees are only considered robust when models satisfy Differential Privacy (DP). Alas, this is not a ubiquitous standard, as many leading companies (and, in fact, research papers) use ad-hoc privacy metrics based on testing the statistical similarity between synthetic and real data. In this paper, we examine the privacy metrics used in real-world synthetic data deployments and demonstrate their unreliability in several ways. First, we provide counter-examples where severe privacy violations occur even if the privacy tests pass and instantiate accurate membership and attribute inference attacks with minimal cost. We then introduce ReconSyn, a reconstruction attack that generates multiple synthetic datasets that are considered private by the metrics but actually leak information unique to individual records. We show that ReconSyn recovers 78-100% of the outliers in the train data with only black-box access to a single fitted generative model and the privacy metrics. In the process, we show that applying DP only to the model does not mitigate this attack, as using privacy metrics breaks the end-to-end DP pipeline. △ Less

Submitted 7 May, 2025; v1 submitted 8 December, 2023; originally announced December 2023.

Comments: Published in the Proceedings of the 46th IEEE Symposium on Security & Privacy (IEEE S&P 2025). Please cite the S&P version

FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting

Authors: Meenatchi Sundaram Muthu Selva Annamalai, Igor Bilogrevic, Emiliano De Cristofaro

Abstract: Browser fingerprinting often provides an attractive alternative to third-party cookies for tracking users across the web. In fact, the increasing restrictions on third-party cookies placed by common web browsers and recent regulations like the GDPR may accelerate the transition. To counter browser fingerprinting, previous work proposed several techniques to detect its prevalence and severity. Howe… ▽ More Browser fingerprinting often provides an attractive alternative to third-party cookies for tracking users across the web. In fact, the increasing restrictions on third-party cookies placed by common web browsers and recent regulations like the GDPR may accelerate the transition. To counter browser fingerprinting, previous work proposed several techniques to detect its prevalence and severity. However, these rely on 1) centralized web crawls and/or 2) computationally intensive operations to extract and process signals (e.g., information-flow and static analysis). To address these limitations, we present FP-Fed, the first distributed system for browser fingerprinting detection. Using FP-Fed, users can collaboratively train on-device models based on their real browsing patterns, without sharing their training data with a central entity, by relying on Differentially Private Federated Learning (DP-FL). To demonstrate its feasibility and effectiveness, we evaluate FP-Fed's performance on a set of 18.3k popular websites with different privacy levels, numbers of participants, and features extracted from the scripts. Our experiments show that FP-Fed achieves reasonably high detection performance and can perform both training and inference efficiently, on-device, by only relying on runtime signals extracted from the execution trace, without requiring any resource-intensive operation. △ Less

Submitted 28 November, 2023; originally announced November 2023.

Journal ref: Published in the Proceedings of the 31st Network and Distributed System Security Symposium (NDSS 2024), please cite accordingly

TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities

Authors: Mohammad Hammas Saeed, Kostantinos Papadamou, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini

Abstract: Alas, coordinated hate attacks, or raids, are becoming increasingly common online. In a nutshell, these are perpetrated by a group of aggressors who organize and coordinate operations on a platform (e.g., 4chan) to target victims on another community (e.g., YouTube). In this paper, we focus on attributing raids to their source community, paving the way for moderation approaches that take the conte… ▽ More Alas, coordinated hate attacks, or raids, are becoming increasingly common online. In a nutshell, these are perpetrated by a group of aggressors who organize and coordinate operations on a platform (e.g., 4chan) to target victims on another community (e.g., YouTube). In this paper, we focus on attributing raids to their source community, paving the way for moderation approaches that take the context (and potentially the motivation) of an attack into consideration. We present TUBERAIDER, an attribution system achieving over 75% accuracy in detecting and attributing coordinated hate attacks on YouTube videos. We instantiate it using links to YouTube videos shared on 4chan's /pol/ board, r/The_Donald, and 16 Incels-related subreddits. We use a peak detector to identify a rise in the comment activity of a YouTube video, which signals that an attack may be occurring. We then train a machine learning classifier based on the community language (i.e., TF-IDF scores of relevant keywords) to perform the attribution. We test TUBERAIDER in the wild and present a few case studies of actual aggression attacks identified by it to showcase its effectiveness. △ Less

Submitted 22 June, 2024; v1 submitted 9 August, 2023; originally announced August 2023.

Comments: Accepted for publication at the 18th International AAAI Conference on Web and Social Media (ICWSM 2024). Please cite accordingly

Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility

Authors: Georgi Ganev, Kai Xu, Emiliano De Cristofaro

Abstract: Generative models trained with Differential Privacy (DP) can produce synthetic data while reducing privacy risks. However, navigating their privacy-utility tradeoffs makes finding the best models for specific settings/tasks challenging. This paper bridges this gap by profiling how DP generative models for tabular data distribute privacy budgets across rows and columns, which is one of the primary… ▽ More Generative models trained with Differential Privacy (DP) can produce synthetic data while reducing privacy risks. However, navigating their privacy-utility tradeoffs makes finding the best models for specific settings/tasks challenging. This paper bridges this gap by profiling how DP generative models for tabular data distribute privacy budgets across rows and columns, which is one of the primary sources of utility degradation. We compare graphical and deep generative models, focusing on the key factors contributing to how privacy budgets are spent, i.e., underlying modeling techniques, DP mechanisms, and data dimensionality. Through our measurement study, we shed light on the characteristics that make different models suitable for various settings and tasks. For instance, we find that graphical models distribute privacy budgets horizontally and thus cannot handle relatively wide datasets for a fixed training time; also, the performance on the task they were optimized for monotonically increases with more data but could also overfit. Deep generative models spend their budgets per iteration, so their behavior is less predictable with varying dataset dimensions, but are more flexible as they could perform better if trained on more features. Moreover, low levels of privacy ($ε\geq100$) could help some models generalize, achieving better results than without applying DP. We believe our work will aid the deployment of DP synthetic data techniques by navigating through the best candidate models vis-a-vis the dataset features, desired privacy levels, and downstream tasks. △ Less

Submitted 28 August, 2024; v1 submitted 18 May, 2023; originally announced May 2023.

Comments: A shorter version of this paper appears in the Proceedings of the 31st ACM Conference on Computer and Communications Security (ACM CCS 2024). This is the full version

BadVFL: Backdoor Attacks in Vertical Federated Learning

Authors: Mohammad Naseri, Yufei Han, Emiliano De Cristofaro

Abstract: Federated learning (FL) enables multiple parties to collaboratively train a machine learning model without sharing their data; rather, they train their own model locally and send updates to a central server for aggregation. Depending on how the data is distributed among the participants, FL can be classified into Horizontal (HFL) and Vertical (VFL). In VFL, the participants share the same set of t… ▽ More Federated learning (FL) enables multiple parties to collaboratively train a machine learning model without sharing their data; rather, they train their own model locally and send updates to a central server for aggregation. Depending on how the data is distributed among the participants, FL can be classified into Horizontal (HFL) and Vertical (VFL). In VFL, the participants share the same set of training instances but only host a different and non-overlapping subset of the whole feature space. Whereas in HFL, each participant shares the same set of features while the training set is split into locally owned training data subsets. VFL is increasingly used in applications like financial fraud detection; nonetheless, very little work has analyzed its security. In this paper, we focus on robustness in VFL, in particular, on backdoor attacks, whereby an adversary attempts to manipulate the aggregate model during the training process to trigger misclassifications. Performing backdoor attacks in VFL is more challenging than in HFL, as the adversary i) does not have access to the labels during training and ii) cannot change the labels as she only has access to the feature embeddings. We present a first-of-its-kind clean-label backdoor attack in VFL, which consists of two phases: a label inference and a backdoor phase. We demonstrate the effectiveness of the attack on three different datasets, investigate the factors involved in its success, and discuss countermeasures to mitigate its impact. △ Less

Submitted 23 August, 2023; v1 submitted 18 April, 2023; originally announced April 2023.

Comments: Accepted for publication at the 45th IEEE Symposium on Security & Privacy (S&P 2024). Please cite accordingly

Beyond Fish and Bicycles: Exploring the Varieties of Online Women's Ideological Spaces

Authors: Utkucan Balci, Chen Ling, Emiliano De Cristofaro, Megan Squire, Gianluca Stringhini, Jeremy Blackburn

Abstract: The Internet has been instrumental in connecting under-represented and vulnerable groups of people. Platforms built to foster social interaction and engagement have enabled historically disenfranchised groups to have a voice. One such vulnerable group is women. In this paper, we explore the diversity in online women's ideological spaces using a multi-dimensional approach. We perform a large-scale,… ▽ More The Internet has been instrumental in connecting under-represented and vulnerable groups of people. Platforms built to foster social interaction and engagement have enabled historically disenfranchised groups to have a voice. One such vulnerable group is women. In this paper, we explore the diversity in online women's ideological spaces using a multi-dimensional approach. We perform a large-scale, data-driven analysis of over 6M Reddit comments and submissions from 14 subreddits. We elicit a diverse taxonomy of online women's ideological spaces, ranging from counterparts to the so-called Manosphere to Gender-Critical Feminism. We then perform content analysis, finding meaningful differences across topics and communities. Finally, we shed light on two platforms, ovarit.com and thepinkpill.co, where two toxic communities of online women's ideological spaces (Gender-Critical Feminism and Femcels) migrated after their ban on Reddit. △ Less

Submitted 13 March, 2023; originally announced March 2023.

Journal ref: Published in the Proceedings of the 15th ACM Web Science Conference 2023 (ACM WebSci 2023). Please cite the WebSci version

Synthetic Data: Methods, Use Cases, and Risks

Authors: Emiliano De Cristofaro

Abstract: Sharing data can often enable compelling applications and analytics. However, more often than not, valuable datasets contain information of a sensitive nature, and thus, sharing them can endanger the privacy of users and organizations. A possible alternative gaining momentum in both the research community and industry is to share synthetic data instead. The idea is to release artificially generate… ▽ More Sharing data can often enable compelling applications and analytics. However, more often than not, valuable datasets contain information of a sensitive nature, and thus, sharing them can endanger the privacy of users and organizations. A possible alternative gaining momentum in both the research community and industry is to share synthetic data instead. The idea is to release artificially generated datasets that resemble the actual data -- more precisely, having similar statistical properties. In this article, we provide a gentle introduction to synthetic data and discuss its use cases, the privacy challenges that are still unaddressed, and its inherent limitations as an effective privacy-enhancing technology. △ Less

Submitted 27 February, 2024; v1 submitted 1 March, 2023; originally announced March 2023.

Comments: To Appear in IEEE Security and Privacy Magazine

LAMBRETTA: Learning to Rank for Twitter Soft Moderation

Authors: Pujan Paudel, Jeremy Blackburn, Emiliano De Cristofaro, Savvas Zannettou, Gianluca Stringhini

Abstract: To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This paper presents LAMBRETTA, a system that automatically identifies tweets that… ▽ More To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This paper presents LAMBRETTA, a system that automatically identifies tweets that are candidates for soft moderation using Learning To Rank (LTR). We run LAMBRETTA on Twitter data to moderate false claims related to the 2020 US Election and find that it flags over 20 times more tweets than Twitter, with only 3.93% false positives and 18.81% false negatives, outperforming alternative state-of-the-art methods based on keyword extraction and semantic search. Overall, LAMBRETTA assists human moderators in identifying and flagging false information on social media. △ Less

Submitted 12 December, 2022; originally announced December 2022.

Comments: 44th IEEE Symposium on Security & Privacy (S&P 2023)

Non-Polar Opposites: Analyzing the Relationship Between Echo Chambers and Hostile Intergroup Interactions on Reddit

Authors: Alexandros Efstratiou, Jeremy Blackburn, Tristan Caulfield, Gianluca Stringhini, Savvas Zannettou, Emiliano De Cristofaro

Abstract: Previous research has documented the existence of both online echo chambers and hostile intergroup interactions. In this paper, we explore the relationship between these two phenomena by studying the activity of 5.97M Reddit users and 421M comments posted over 13 years. We examine whether users who are more engaged in echo chambers are more hostile when they comment on other communities. We then c… ▽ More Previous research has documented the existence of both online echo chambers and hostile intergroup interactions. In this paper, we explore the relationship between these two phenomena by studying the activity of 5.97M Reddit users and 421M comments posted over 13 years. We examine whether users who are more engaged in echo chambers are more hostile when they comment on other communities. We then create a typology of relationships between political communities based on whether their users are toxic to each other, whether echo chamber-like engagement with these communities is associated with polarization, and on the communities' political leanings. We observe both the echo chamber and hostile intergroup interaction phenomena, but neither holds universally across communities. Contrary to popular belief, we find that polarizing and toxic speech is more dominant between communities on the same, rather than opposing, sides of the political spectrum, especially on the left; however, this mainly points to the collective targeting of political outgroups. △ Less

Submitted 25 November, 2022; originally announced November 2022.

Journal ref: 17th International AAAI Conference on Web and Social Media (ICWSM 2023). Please cite accordingly

Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots

Authors: Wai Man Si, Michael Backes, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, Savvas Zannettou, Yang Zhang

Abstract: Chatbots are used in many applications, e.g., automated agents, smart home assistants, interactive characters in online games, etc. Therefore, it is crucial to ensure they do not behave in undesired manners, providing offensive or toxic responses to users. This is not a trivial task as state-of-the-art chatbot models are trained on large, public datasets openly collected from the Internet. This pa… ▽ More Chatbots are used in many applications, e.g., automated agents, smart home assistants, interactive characters in online games, etc. Therefore, it is crucial to ensure they do not behave in undesired manners, providing offensive or toxic responses to users. This is not a trivial task as state-of-the-art chatbot models are trained on large, public datasets openly collected from the Internet. This paper presents a first-of-its-kind, large-scale measurement of toxicity in chatbots. We show that publicly available chatbots are prone to providing toxic responses when fed toxic queries. Even more worryingly, some non-toxic queries can trigger toxic responses too. We then set out to design and experiment with an attack, ToxicBuddy, which relies on fine-tuning GPT-2 to generate non-toxic queries that make chatbots respond in a toxic manner. Our extensive experimental evaluation demonstrates that our attack is effective against public chatbot models and outperforms manually-crafted malicious queries proposed by previous work. We also evaluate three defense mechanisms against ToxicBuddy, showing that they either reduce the attack performance at the cost of affecting the chatbot's utility or are only effective at mitigating a portion of the attack. This highlights the need for more research from the computer security and online safety communities to ensure that chatbot models do not hurt their users. Overall, we are confident that ToxicBuddy can be used as an auditing tool and that our work will pave the way toward designing more effective defenses for chatbot safety. △ Less

Submitted 9 September, 2022; v1 submitted 7 September, 2022; originally announced September 2022.

Journal ref: Published in ACM CCS 2022. Please cite the CCS version

Cerberus: Exploring Federated Prediction of Security Events

Authors: Mohammad Naseri, Yufei Han, Enrico Mariconti, Yun Shen, Gianluca Stringhini, Emiliano De Cristofaro

Abstract: Modern defenses against cyberattacks increasingly rely on proactive approaches, e.g., to predict the adversary's next actions based on past events. Building accurate prediction models requires knowledge from many organizations; alas, this entails disclosing sensitive information, such as network structures, security postures, and policies, which might often be undesirable or outright impossible. I… ▽ More Modern defenses against cyberattacks increasingly rely on proactive approaches, e.g., to predict the adversary's next actions based on past events. Building accurate prediction models requires knowledge from many organizations; alas, this entails disclosing sensitive information, such as network structures, security postures, and policies, which might often be undesirable or outright impossible. In this paper, we explore the feasibility of using Federated Learning (FL) to predict future security events. To this end, we introduce Cerberus, a system enabling collaborative training of Recurrent Neural Network (RNN) models for participating organizations. The intuition is that FL could potentially offer a middle-ground between the non-private approach where the training data is pooled at a central server and the low-utility alternative of only training local models. We instantiate Cerberus on a dataset obtained from a major security company's intrusion prevention product and evaluate it vis-a-vis utility, robustness, and privacy, as well as how participants contribute to and benefit from the system. Overall, our work sheds light on both the positive aspects and the challenges of using FL for this task and paves the way for deploying federated approaches to predictive security. △ Less

Submitted 7 September, 2022; originally announced September 2022.

Journal ref: Proceedings of the 29th ACM Conference on Computer and Communications Security (ACM CCS 2022)

Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes

Authors: Alexandros Efstratiou, Emiliano De Cristofaro

Abstract: Previous work suggests that people's preference for different kinds of information depends on more than just accuracy. This could happen because the messages contained within different pieces of information may either be well-liked or repulsive. Whereas factual information must often convey uncomfortable truths, misinformation can have little regard for veracity and leverage psychological processe… ▽ More Previous work suggests that people's preference for different kinds of information depends on more than just accuracy. This could happen because the messages contained within different pieces of information may either be well-liked or repulsive. Whereas factual information must often convey uncomfortable truths, misinformation can have little regard for veracity and leverage psychological processes which increase its attractiveness and proliferation on social media. In this review, we argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation by reducing, rather than increasing, the psychological cost of doing so. We cover how attention may often be shifted away from accuracy and towards other goals, how social and individual cognition is affected by misinformation and the cases under which debunking it is most effective, and how the formation of online groups affects information consumption patterns, often leading to more polarization and radicalization. Throughout, we make the case that polarization and misinformation adherence are closely tied. We identify ways in which the psychological cost of adhering to misinformation can be increased when designing anti-misinformation interventions or resilient affordances, and we outline open research questions that the CSCW community can take up in further understanding this cost. △ Less

Submitted 30 June, 2022; originally announced June 2022.

Journal ref: 25th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2022)

Toxicity in the Decentralized Web and the Potential for Model Sharing

Authors: Haris Bin Zia, Aravindh. Raman, Ignacio Castro, Ishaku Hassan Anaobi, Emiliano De Cristofaro, Nishanth Sastry, Gareth Tyson

Abstract: The "Decentralised Web" (DW) is an evolving concept, which encompasses technologies aimed at providing greater transparency and openness on the web. The DW relies on independent servers (aka instances) that mesh together in a peer-to-peer fashion to deliver a range of services (e.g. micro-blogs, image sharing, video streaming). However, toxic content moderation in this decentralised context is cha… ▽ More The "Decentralised Web" (DW) is an evolving concept, which encompasses technologies aimed at providing greater transparency and openness on the web. The DW relies on independent servers (aka instances) that mesh together in a peer-to-peer fashion to deliver a range of services (e.g. micro-blogs, image sharing, video streaming). However, toxic content moderation in this decentralised context is challenging. This is because there is no central entity that can define toxicity, nor a large central pool of data that can be used to build universal classifiers. It is therefore unsurprising that there have been several high-profile cases of the DW being misused to coordinate and disseminate harmful material. Using a dataset of 9.9M posts from 117K users on Pleroma (a popular DW microblogging service), we quantify the presence of toxic content. We find that toxic content is prevalent and spreads rapidly between instances. We show that automating per-instance content moderation is challenging due to the lack of sufficient training data available and the effort required in labelling. We therefore propose and evaluate ModPair, a model sharing system that effectively detects toxic content, gaining an average per-instance macro-F1 score 0.89. △ Less

Submitted 27 April, 2022; originally announced April 2022.

Journal ref: Published in the Proceedings of the 2022 ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS'22). Please cite accordingly

Feels Bad Man: Dissecting Automated Hateful Meme Detection Through the Lens of Facebook's Challenge

Authors: Catherine Jennifer, Fatemeh Tahmasbi, Jeremy Blackburn, Gianluca Stringhini, Savvas Zannettou, Emiliano De Cristofaro

Abstract: Internet memes have become a dominant method of communication; at the same time, however, they are also increasingly being used to advocate extremism and foster derogatory beliefs. Nonetheless, we do not have a firm understanding as to which perceptual aspects of memes cause this phenomenon. In this work, we assess the efficacy of current state-of-the-art multimodal machine learning models toward… ▽ More Internet memes have become a dominant method of communication; at the same time, however, they are also increasingly being used to advocate extremism and foster derogatory beliefs. Nonetheless, we do not have a firm understanding as to which perceptual aspects of memes cause this phenomenon. In this work, we assess the efficacy of current state-of-the-art multimodal machine learning models toward hateful meme detection, and in particular with respect to their generalizability across platforms. We use two benchmark datasets comprising 12,140 and 10,567 images from 4chan's "Politically Incorrect" board (/pol/) and Facebook's Hateful Memes Challenge dataset to train the competition's top-ranking machine learning models for the discovery of the most prominent features that distinguish viral hateful memes from benign ones. We conduct three experiments to determine the importance of multimodality on classification performance, the influential capacity of fringe Web communities on mainstream social platforms and vice versa, and the models' learning transferability on 4chan memes. Our experiments show that memes' image characteristics provide a greater wealth of information than its textual content. We also find that current systems developed for online detection of hate speech in memes necessitate further concentration on its visual elements to improve their interpretation of underlying cultural connotations, implying that multimodal models fail to adequately grasp the intricacies of hate speech in memes and generalize across social media platforms. △ Less

Submitted 17 February, 2022; originally announced February 2022.

TROLLMAGNIFIER: Detecting State-Sponsored Troll Accounts on Reddit

Authors: Mohammad Hammas Saeed, Shiza Ali, Jeremy Blackburn, Emiliano De Cristofaro, Savvas Zannettou, Gianluca Stringhini

Abstract: Growing evidence points to recurring influence campaigns on social media, often sponsored by state actors aiming to manipulate public opinion on sensitive political topics. Typically, campaigns are performed through instrumented accounts, known as troll accounts; despite their prominence, however, little work has been done to detect these accounts in the wild. In this paper, we present TROLLMAGNIF… ▽ More Growing evidence points to recurring influence campaigns on social media, often sponsored by state actors aiming to manipulate public opinion on sensitive political topics. Typically, campaigns are performed through instrumented accounts, known as troll accounts; despite their prominence, however, little work has been done to detect these accounts in the wild. In this paper, we present TROLLMAGNIFIER, a detection system for troll accounts. Our key observation, based on analysis of known Russian-sponsored troll accounts identified by Reddit, is that they show loose coordination, often interacting with each other to further specific narratives. Therefore, troll accounts controlled by the same actor often show similarities that can be leveraged for detection. TROLLMAGNIFIER learns the typical behavior of known troll accounts and identifies more that behave similarly. We train TROLLMAGNIFIER on a set of 335 known troll accounts and run it on a large dataset of Reddit accounts. Our system identifies 1,248 potential troll accounts; we then provide a multi-faceted analysis to corroborate the correctness of our classification. In particular, 66% of the detected accounts show signs of being instrumented by malicious actors (e.g., they were created on the same exact day as a known troll, they have since been suspended by Reddit, etc.). They also discuss similar topics as the known troll accounts and exhibit temporal synchronization in their activity. Overall, we show that using TROLLMAGNIFIER, one can grow the initial knowledge of potential trolls provided by Reddit by over 300%. △ Less

Submitted 1 December, 2021; originally announced December 2021.

Understanding the Use of e-Prints on Reddit and 4chan's Politically Incorrect Board

Authors: Satrio Baskoro Yudhoatmojo, Emiliano De Cristofaro, Jeremy Blackburn

Abstract: The dissemination and reach of scientific knowledge have increased at a blistering pace. In this context, e-Print servers have played a central role by providing scientists with a rapid and open mechanism for disseminating research without waiting for the (lengthy) peer review process. While helping the scientific community in several ways, e-Print servers also provide scientific communicators and… ▽ More The dissemination and reach of scientific knowledge have increased at a blistering pace. In this context, e-Print servers have played a central role by providing scientists with a rapid and open mechanism for disseminating research without waiting for the (lengthy) peer review process. While helping the scientific community in several ways, e-Print servers also provide scientific communicators and the general public with access to a wealth of knowledge without paying hefty subscription fees. This motivates us to study how e-Prints are positioned within Web community discussions. In this paper, we analyze data from two Web communities: 14 years of Reddit data and over 4 from 4chan's Politically Incorrect board. Our findings highlight the presence of e-Prints in both science-enthusiast and general-audience communities. Real-world events and distinct factors influence the e-Prints people's discussions; e.g., there was a surge of COVID-19-related research publications during the early months of the outbreak and increased references to e-Prints in online discussions. Text in e-Prints and in online discussions referencing them has a low similarity, suggesting that the latter are not exclusively talking about the findings in the former. Further, our analysis of a sample of threads highlights: 1) misinterpretation and generalization of research findings, 2) early research findings being amplified as a source for future predictions, and 3) questioning findings from a pseudoscientific e-Print. Overall, our work emphasizes the need to quickly and effectively validate non-peer-reviewed e-Prints that get substantial press/social media coverage to help mitigate wrongful interpretations of scientific outputs. △ Less

Submitted 8 March, 2023; v1 submitted 3 November, 2021; originally announced November 2021.

Journal ref: Published in the Proceedings of the 15th ACM Web Science Conference 2023 (ACM WebSci 2023). Please cite the WebSci version

Slapping Cats, Bopping Heads, and Oreo Shakes: Understanding Indicators of Virality in TikTok Short Videos

Authors: Chen Ling, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini

Abstract: Short videos have become one of the leading media used by younger generations to express themselves online and thus a driving force in shaping online culture. In this context, TikTok has emerged as a platform where viral videos are often posted first. In this paper, we study what elements of short videos posted on TikTok contribute to their virality. We apply a mixed-method approach to develop a c… ▽ More Short videos have become one of the leading media used by younger generations to express themselves online and thus a driving force in shaping online culture. In this context, TikTok has emerged as a platform where viral videos are often posted first. In this paper, we study what elements of short videos posted on TikTok contribute to their virality. We apply a mixed-method approach to develop a codebook and identify important virality features. We do so vis-à-vis three research hypotheses; namely, that: 1) the video content, 2) TikTok's recommendation algorithm, and 3) the popularity of the video creator contribute to virality. We collect and label a dataset of 400 TikTok videos and train classifiers to help us identify the features that influence virality the most. While the number of followers is the most powerful predictor, close-up and medium-shot scales also play an essential role. So does the lifespan of the video, the presence of text, and the point of view. Our research highlights the characteristics that distinguish viral from non-viral TikTok videos, laying the groundwork for developing additional approaches to create more engaging online content and proactively identify possibly risky content that is likely to reach a large audience. △ Less

Submitted 3 November, 2021; originally announced November 2021.

Soros, Child Sacrifices, and 5G: Understanding the Spread of Conspiracy Theories on Web Communities

Authors: Pujan Paudel, Jeremy Blackburn, Emiliano De Cristofaro, Savvas Zannettou, Gianluca Stringhini

Abstract: This paper presents a multi-platform computational pipeline geared to identify social media posts discussing (known) conspiracy theories. We use 189 conspiracy claims collected by Snopes, and find 66k posts and 277k comments on Reddit, and 379k tweets discussing them. Then, we study how conspiracies are discussed on different Web communities and which ones are particularly influential in driving t… ▽ More This paper presents a multi-platform computational pipeline geared to identify social media posts discussing (known) conspiracy theories. We use 189 conspiracy claims collected by Snopes, and find 66k posts and 277k comments on Reddit, and 379k tweets discussing them. Then, we study how conspiracies are discussed on different Web communities and which ones are particularly influential in driving the discussion about them. Our analysis sheds light on how conspiracy theories are discussed and spread online, while highlighting multiple challenges in mitigating them. △ Less

Submitted 3 November, 2021; originally announced November 2021.

Exploring Content Moderation in the Decentralised Web: The Pleroma Case

Authors: Anaobi Ishaku Hassan, Aravindh Raman, Ignacio Castro, Haris Bin Zia, Emiliano De Cristofaro, Nishanth Sastry, Gareth Tyson

Abstract: Decentralising the Web is a desirable but challenging goal. One particular challenge is achieving decentralised content moderation in the face of various adversaries (e.g. trolls). To overcome this challenge, many Decentralised Web (DW) implementations rely on federation policies. Administrators use these policies to create rules that ban or modify content that matches specific rules. This, howeve… ▽ More Decentralising the Web is a desirable but challenging goal. One particular challenge is achieving decentralised content moderation in the face of various adversaries (e.g. trolls). To overcome this challenge, many Decentralised Web (DW) implementations rely on federation policies. Administrators use these policies to create rules that ban or modify content that matches specific rules. This, however, can have unintended consequences for many users. In this paper, we present the first study of federation policies on the DW, their in-the-wild usage, and their impact on users. We identify how these policies may negatively impact "innocent" users and outline possible solutions to avoid this problem in the future. △ Less

Submitted 30 October, 2021; v1 submitted 26 October, 2021; originally announced October 2021.

Journal ref: Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies (ACM CoNext 2021)

Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data

Authors: Georgi Ganev, Bristena Oprisanu, Emiliano De Cristofaro

Abstract: Generative models trained with Differential Privacy (DP) can be used to generate synthetic data while minimizing privacy risks. We analyze the impact of DP on these models vis-a-vis underrepresented classes/subgroups of data, specifically, studying: 1) the size of classes/subgroups in the synthetic data, and 2) the accuracy of classification tasks run on them. We also evaluate the effect of variou… ▽ More Generative models trained with Differential Privacy (DP) can be used to generate synthetic data while minimizing privacy risks. We analyze the impact of DP on these models vis-a-vis underrepresented classes/subgroups of data, specifically, studying: 1) the size of classes/subgroups in the synthetic data, and 2) the accuracy of classification tasks run on them. We also evaluate the effect of various levels of imbalance and privacy budgets. Our analysis uses three state-of-the-art DP models (PrivBayes, DP-WGAN, and PATE-GAN) and shows that DP yields opposite size distributions in the generated synthetic data. It affects the gap between the majority and minority classes/subgroups; in some cases by reducing it (a "Robin Hood" effect) and, in others, by increasing it (a "Matthew" effect). Either way, this leads to (similar) disparate impacts on the accuracy of classification tasks on the synthetic data, affecting disproportionately more the underrepresented subparts of the data. Consequently, when training models on synthetic data, one might incur the risk of treating different subpopulations unevenly, leading to unreliable or unfair conclusions. △ Less

Submitted 26 June, 2022; v1 submitted 23 September, 2021; originally announced September 2021.

Journal ref: Proceedings of the 39th International Conference on Machine Learning (ICML 2022)

An Early Look at the Gettr Social Network

Authors: Pujan Paudel, Jeremy Blackburn, Emiliano De Cristofaro, Savvas Zannettou, Gianluca Stringhini

Abstract: This paper presents the first data-driven analysis of Gettr, a new social network platform launched by former US President Donald Trump's team. Among other things, we find that users on the platform heavily discuss politics, with a focus on the Trump campaign in the US and Bolsonaro's in Brazil. Activity on the platform has steadily been decreasing since its launch, although a core of verified use… ▽ More This paper presents the first data-driven analysis of Gettr, a new social network platform launched by former US President Donald Trump's team. Among other things, we find that users on the platform heavily discuss politics, with a focus on the Trump campaign in the US and Bolsonaro's in Brazil. Activity on the platform has steadily been decreasing since its launch, although a core of verified users and early adopters kept posting and become central to it. Finally, although toxicity has been increasing over time, the average level of toxicity is still lower than the one recently observed on other fringe social networks like Gab and 4chan. Overall, we provide a first quantitative look at this new community, observing a lack of organic engagement and activity. △ Less

Submitted 12 August, 2021; originally announced August 2021.

"I'm a Professor, which isn't usually a dangerous job": Internet-Facilitated Harassment and its Impact on Researchers

Authors: Periwinkle Doerfler, Andrea Forte, Emiliano De Cristofaro, Gianluca Stringhini, Jeremy Blackburn, Damon McCoy

Abstract: While the Internet has dramatically increased the exposure that research can receive, it has also facilitated harassment against scholars. To understand the impact that these attacks can have on the work of researchers, we perform a series of systematic interviews with researchers including academics, journalists, and activists, who have experienced targeted, Internet-facilitated harassment. We pr… ▽ More While the Internet has dramatically increased the exposure that research can receive, it has also facilitated harassment against scholars. To understand the impact that these attacks can have on the work of researchers, we perform a series of systematic interviews with researchers including academics, journalists, and activists, who have experienced targeted, Internet-facilitated harassment. We provide a framework for understanding the types of harassers that target researchers, the harassment that ensues, and the personal and professional impact on individuals and academic freedom. We then study preventative and remedial strategies available, and the institutions that prevent some of these strategies from being more effective. Finally, we discuss the ethical structures that could facilitate more equitable access to participating in research without serious personal suffering. △ Less

Submitted 22 April, 2021; v1 submitted 22 April, 2021; originally announced April 2021.

A Multi-Platform Analysis of Political News Discussion and Sharing on Web Communities

Authors: Yuping Wang, Savvas Zannettou, Jeremy Blackburn, Barry Bradlyn, Emiliano De Cristofaro, Gianluca Stringhini

Abstract: The news ecosystem has become increasingly complex, encompassing a wide range of sources with varying levels of trustworthiness, and with public commentary giving different spins to the same stories. In this paper, we present a multi-platform measurement of this ecosystem. We compile a list of 1,073 news websites and extract posts from four Web communities (Twitter, Reddit, 4chan, and Gab) that co… ▽ More The news ecosystem has become increasingly complex, encompassing a wide range of sources with varying levels of trustworthiness, and with public commentary giving different spins to the same stories. In this paper, we present a multi-platform measurement of this ecosystem. We compile a list of 1,073 news websites and extract posts from four Web communities (Twitter, Reddit, 4chan, and Gab) that contain URLs from these sources. This yields a dataset of 38M posts containing 15M news URLs, spanning almost three years. We study the data along several axes, assessing the trustworthiness of shared news, designing a method to group news articles into stories, analyzing these stories are discussed and measuring the influence various Web communities have in that. Our analysis shows that different communities discuss different types of news, with polarized communities like Gab and /r/The_Donald subreddit disproportionately referencing untrustworthy sources. We also find that fringe communities often have a disproportionate influence on other platforms w.r.t. pushing narratives around certain news, for example about political elections, immigration, or foreign policy. △ Less

Submitted 5 March, 2021; originally announced March 2021.

On Utility and Privacy in Synthetic Genomic Data

Authors: Bristena Oprisanu, Georgi Ganev, Emiliano De Cristofaro

Abstract: The availability of genomic data is essential to progress in biomedical research, personalized medicine, etc. However, its extreme sensitivity makes it problematic, if not outright impossible, to publish or share it. As a result, several initiatives have been launched to experiment with synthetic genomic data, e.g., using generative models to learn the underlying distribution of the real data and… ▽ More The availability of genomic data is essential to progress in biomedical research, personalized medicine, etc. However, its extreme sensitivity makes it problematic, if not outright impossible, to publish or share it. As a result, several initiatives have been launched to experiment with synthetic genomic data, e.g., using generative models to learn the underlying distribution of the real data and generate artificial datasets that preserve its salient characteristics without exposing it. This paper provides the first evaluation of both utility and privacy protection of six state-of-the-art models for generating synthetic genomic data. We assess the performance of the synthetic data on several common tasks, such as allele population statistics and linkage disequilibrium. We then measure privacy through the lens of membership inference attacks, i.e., inferring whether a record was part of the training data. Our experiments show that no single approach to generate synthetic genomic data yields both high utility and strong privacy across the board. Also, the size and nature of the training dataset matter. Moreover, while some combinations of datasets and models produce synthetic data with distributions close to the real data, there often are target data points that are vulnerable to membership inference. Looking forward, our techniques can be used by practitioners to assess the risks of deploying synthetic genomic data in the wild and serve as a benchmark for future work. △ Less

Submitted 18 January, 2022; v1 submitted 5 February, 2021; originally announced February 2021.

Comments: Published in the Proceedings of the 29th Network and Distributed System Security Symposium (NDSS 2022)

ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models

Authors: Yugeng Liu, Rui Wen, Xinlei He, Ahmed Salem, Zhikun Zhang, Michael Backes, Emiliano De Cristofaro, Mario Fritz, Yang Zhang

Abstract: Inference attacks against Machine Learning (ML) models allow adversaries to learn sensitive information about training data, model parameters, etc. While researchers have studied, in depth, several kinds of attacks, they have done so in isolation. As a result, we lack a comprehensive picture of the risks caused by the attacks, e.g., the different scenarios they can be applied to, the common factor… ▽ More Inference attacks against Machine Learning (ML) models allow adversaries to learn sensitive information about training data, model parameters, etc. While researchers have studied, in depth, several kinds of attacks, they have done so in isolation. As a result, we lack a comprehensive picture of the risks caused by the attacks, e.g., the different scenarios they can be applied to, the common factors that influence their performance, the relationship among them, or the effectiveness of possible defenses. In this paper, we fill this gap by presenting a first-of-its-kind holistic risk assessment of different inference attacks against machine learning models. We concentrate on four attacks -- namely, membership inference, model inversion, attribute inference, and model stealing -- and establish a threat model taxonomy. Our extensive experimental evaluation, run on five model architectures and four image datasets, shows that the complexity of the training dataset plays an important role with respect to the attack's performance, while the effectiveness of model stealing and membership inference attacks are negatively correlated. We also show that defenses like DP-SGD and Knowledge Distillation can only mitigate some of the inference attacks. Our analysis relies on a modular re-usable software, ML-Doctor, which enables ML model owners to assess the risks of deploying their models, and equally serves as a benchmark tool for researchers and practitioners. △ Less

Submitted 6 October, 2021; v1 submitted 4 February, 2021; originally announced February 2021.

The Gospel According to Q: Understanding the QAnon Conspiracy from the Perspective of Canonical Information

Authors: Antonis Papasavva, Max Aliapoulios, Cameron Ballard, Emiliano De Cristofaro, Gianluca Stringhini, Savvas Zannettou, Jeremy Blackburn

Abstract: The QAnon conspiracy theory claims that a cabal of (literally) blood-thirsty politicians and media personalities are engaged in a war to destroy society. By interpreting cryptic "drops" of information from an anonymous insider calling themself Q, adherents of the conspiracy theory believe that Donald Trump is leading them in an active fight against this cabal. QAnon has been covered extensively by… ▽ More The QAnon conspiracy theory claims that a cabal of (literally) blood-thirsty politicians and media personalities are engaged in a war to destroy society. By interpreting cryptic "drops" of information from an anonymous insider calling themself Q, adherents of the conspiracy theory believe that Donald Trump is leading them in an active fight against this cabal. QAnon has been covered extensively by the media, as its adherents have been involved in multiple violent acts, including the January 6th, 2021 seditious storming of the US Capitol building. Nevertheless, we still have relatively little understanding of how the theory evolved and spread on the Web, and the role played in that by multiple platforms. To address this gap, we study QAnon from the perspective of "Q" themself. We build a dataset of 4,949 canonical Q drops collected from six "aggregation sites," which curate and archive them from their original posting to anonymous and ephemeral image boards. We expose that these sites have a relatively low (overall) agreement, and thus at least some Q drops should probably be considered apocryphal. We then analyze the Q drops' contents to identify topics of discussion and find statistically significant indications that drops were not authored by a single individual. Finally, we look at how posts on Reddit are used to disseminate Q drops to wider audiences. We find that dissemination was (initially) limited to a few sub-communities and that, while heavy-handed moderation decisions have reduced the overall issue, the "gospel" of Q persists on the Web. △ Less

Submitted 29 April, 2022; v1 submitted 21 January, 2021; originally announced January 2021.

Journal ref: Published in the Proceedings of the 16th International AAAI Conference on Web and Social Media (ICWSM 2022). Please cite accordingly

Dissecting the Meme Magic: Understanding Indicators of Virality in Image Memes

Authors: Chen Ling, Ihab AbuHilal, Jeremy Blackburn, Emiliano De Cristofaro, Savvas Zannettou, Gianluca Stringhini

Abstract: Despite the increasingly important role played by image memes, we do not yet have a solid understanding of the elements that might make a meme go viral on social media. In this paper, we investigate what visual elements distinguish image memes that are highly viral on social media from those that do not get re-shared, across three dimensions: composition, subjects, and target audience. Drawing fro… ▽ More Despite the increasingly important role played by image memes, we do not yet have a solid understanding of the elements that might make a meme go viral on social media. In this paper, we investigate what visual elements distinguish image memes that are highly viral on social media from those that do not get re-shared, across three dimensions: composition, subjects, and target audience. Drawing from research in art theory, psychology, marketing, and neuroscience, we develop a codebook to characterize image memes, and use it to annotate a set of 100 image memes collected from 4chan's Politically Incorrect Board (/pol/). On the one hand, we find that highly viral memes are more likely to use a close-up scale, contain characters, and include positive or negative emotions. On the other hand, image memes that do not present a clear subject the viewer can focus attention on, or that include long text are not likely to be re-shared by users. We train machine learning models to distinguish between image memes that are likely to go viral and those that are unlikely to be re-shared, obtaining an AUC of 0.866 on our dataset. We also show that the indicators of virality identified by our model can help characterize the most viral memes posted on mainstream online social networks too, as our classifiers are able to predict 19 out of the 20 most popular image memes posted on Twitter and Reddit between 2016 and 2018. Overall, our analysis sheds light on what indicators characterize viral and non-viral visual content online, and set the basis for developing better techniques to create or moderate content that is more likely to catch the viewer's attention. △ Less

Submitted 16 January, 2021; originally announced January 2021.

Comments: To appear at the 24th ACM Conference on Computer-Supported Coop- erative Work and Social Computing (CSCW 2021)

An Early Look at the Parler Online Social Network

Authors: Max Aliapoulios, Emmi Bevensee, Jeremy Blackburn, Barry Bradlyn, Emiliano De Cristofaro, Gianluca Stringhini, Savvas Zannettou

Abstract: Parler is as an "alternative" social network promoting itself as a service that allows to "speak freely and express yourself openly, without fear of being deplatformed for your views." Because of this promise, the platform become popular among users who were suspended on mainstream social networks for violating their terms of service, as well as those fearing censorship. In particular, the service… ▽ More Parler is as an "alternative" social network promoting itself as a service that allows to "speak freely and express yourself openly, without fear of being deplatformed for your views." Because of this promise, the platform become popular among users who were suspended on mainstream social networks for violating their terms of service, as well as those fearing censorship. In particular, the service was endorsed by several conservative public figures, encouraging people to migrate from traditional social networks. After the storming of the US Capitol on January 6, 2021, Parler has been progressively deplatformed, as its app was removed from Apple/Google Play stores and the website taken down by the hosting provider. This paper presents a dataset of 183M Parler posts made by 4M users between August 2018 and January 2021, as well as metadata from 13.25M user profiles. We also present a basic characterization of the dataset, which shows that the platform has witnessed large influxes of new users after being endorsed by popular figures, as well as a reaction to the 2020 US Presidential Election. We also show that discussion on the platform is dominated by conservative topics, President Trump, as well as conspiracy theories like QAnon. △ Less

Submitted 18 February, 2021; v1 submitted 11 January, 2021; originally announced January 2021.

Journal ref: Proceedings of the International AAAI Conference on Web and Social Media, 15(1), 943--951 (2021)

"It is just a flu": Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations

Authors: Kostantinos Papadamou, Savvas Zannettou, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, Michael Sirivianos

Abstract: The role played by YouTube's recommendation algorithm in unwittingly promoting misinformation and conspiracy theories is not entirely understood. Yet, this can have dire real-world consequences, especially when pseudoscientific content is promoted to users at critical times, such as the COVID-19 pandemic. In this paper, we set out to characterize and detect pseudoscientific misinformation on YouTu… ▽ More The role played by YouTube's recommendation algorithm in unwittingly promoting misinformation and conspiracy theories is not entirely understood. Yet, this can have dire real-world consequences, especially when pseudoscientific content is promoted to users at critical times, such as the COVID-19 pandemic. In this paper, we set out to characterize and detect pseudoscientific misinformation on YouTube. We collect 6.6K videos related to COVID-19, the Flat Earth theory, as well as the anti-vaccination and anti-mask movements. Using crowdsourcing, we annotate them as pseudoscience, legitimate science, or irrelevant and train a deep learning classifier to detect pseudoscientific videos with an accuracy of 0.79. We quantify user exposure to this content on various parts of the platform and how this exposure changes based on the user's watch history. We find that YouTube suggests more pseudoscientific content regarding traditional pseudoscientific topics (e.g., flat earth, anti-vaccination) than for emerging ones (like COVID-19). At the same time, these recommendations are more common on the search results page than on a user's homepage or in the recommendation section when actively watching videos. Finally, we shed light on how a user's watch history substantially affects the type of recommended videos. △ Less

Submitted 12 October, 2021; v1 submitted 22 October, 2020; originally announced October 2020.

Comments: To appear at the 16th International Conference on Web and Social Media (ICWSM 2022). Please cite the ICWSM version

Do Platform Migrations Compromise Content Moderation? Evidence from r/The_Donald and r/Incels

Authors: Manoel Horta Ribeiro, Shagun Jhaver, Savvas Zannettou, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, Robert West

Abstract: When toxic online communities on mainstream platforms face moderation measures, such as bans, they may migrate to other platforms with laxer policies or set up their own dedicated websites. Previous work suggests that within mainstream platforms, community-level moderation is effective in mitigating the harm caused by the moderated communities. It is, however, unclear whether these results also ho… ▽ More When toxic online communities on mainstream platforms face moderation measures, such as bans, they may migrate to other platforms with laxer policies or set up their own dedicated websites. Previous work suggests that within mainstream platforms, community-level moderation is effective in mitigating the harm caused by the moderated communities. It is, however, unclear whether these results also hold when considering the broader Web ecosystem. Do toxic communities continue to grow in terms of their user base and activity on the new platforms? Do their members become more toxic and ideologically radicalized? In this paper, we report the results of a large-scale observational study of how problematic online communities progress following community-level moderation measures. We analyze data from r/The_Donald and r/Incels, two communities that were banned from Reddit and subsequently migrated to their own standalone websites. Our results suggest that, in both cases, moderation measures significantly decreased posting activity on the new platform, reducing the number of posts, active users, and newcomers. In spite of that, users in one of the studied communities (r/The_Donald) showed increases in signals associated with toxicity and radicalization, which justifies concerns that the reduction in activity may come at the expense of a more toxic and radical community. Overall, our results paint a nuanced portrait of the consequences of community-level moderation and can inform their design and deployment. △ Less

Submitted 20 August, 2021; v1 submitted 20 October, 2020; originally announced October 2020.

Comments: This paper has been accepted at CSCW 2021, please cite accordingly

Understanding the Use of Fauxtography on Social Media

Authors: Yuping Wang, Fatemeh Tahmasbi, Jeremy Blackburn, Barry Bradlyn, Emiliano De Cristofaro, David Magerman, Savvas Zannettou, Gianluca Stringhini

Abstract: Despite the influence that image-based communication has on online discourse, the role played by images in disinformation is still not well understood. In this paper, we present the first large-scale study of fauxtography, analyzing the use of manipulated or misleading images in news discussion on online communities. First, we develop a computational pipeline geared to detect fauxtography, and ide… ▽ More Despite the influence that image-based communication has on online discourse, the role played by images in disinformation is still not well understood. In this paper, we present the first large-scale study of fauxtography, analyzing the use of manipulated or misleading images in news discussion on online communities. First, we develop a computational pipeline geared to detect fauxtography, and identify over 61k instances of fauxtography discussed on Twitter, 4chan, and Reddit. Then, we study how posting fauxtography affects engagement of posts on social media, finding that posts containing it receive more interactions in the form of re-shares, likes, and comments. Finally, we show that fauxtography images are often turned into memes by Web communities. Our findings show that effective mitigation against disinformation need to take images into account, and highlight a number of challenges in dealing with image-based disinformation. △ Less

Submitted 25 September, 2020; v1 submitted 24 September, 2020; originally announced September 2020.

"Is it a Qoincidence?": An Exploratory Study of QAnon on Voat

Authors: Antonis Papasavva, Jeremy Blackburn, Gianluca Stringhini, Savvas Zannettou, Emiliano De Cristofaro

Abstract: Online fringe communities offer fertile grounds for users seeking and sharing ideas fueling suspicion of mainstream news and conspiracy theories. Among these, the QAnon conspiracy theory emerged in 2017 on 4chan, broadly supporting the idea that powerful politicians, aristocrats, and celebrities are closely engaged in a global pedophile ring. Simultaneously, governments are thought to be controlle… ▽ More Online fringe communities offer fertile grounds for users seeking and sharing ideas fueling suspicion of mainstream news and conspiracy theories. Among these, the QAnon conspiracy theory emerged in 2017 on 4chan, broadly supporting the idea that powerful politicians, aristocrats, and celebrities are closely engaged in a global pedophile ring. Simultaneously, governments are thought to be controlled by "puppet masters," as democratically elected officials serve as a fake showroom of democracy. This paper provides an empirical exploratory analysis of the QAnon community on Voat.co, a Reddit-esque news aggregator, which has captured the interest of the press for its toxicity and for providing a platform to QAnon followers. More precisely, we analyze a large dataset from /v/GreatAwakening, the most popular QAnon-related subverse (the Voat equivalent of a subreddit), to characterize activity and user engagement. To further understand the discourse around QAnon, we study the most popular named entities mentioned in the posts, along with the most prominent topics of discussion, which focus on US politics, Donald Trump, and world events. We also use word embeddings to identify narratives around QAnon-specific keywords. Our graph visualization shows that some of the QAnon-related ones are closely related to those from the Pizzagate conspiracy theory and so-called drops by "Q." Finally, we analyze content toxicity, finding that discussions on /v/GreatAwakening are less toxic than in the broad Voat community. △ Less

Submitted 14 February, 2021; v1 submitted 10 September, 2020; originally announced September 2020.

Journal ref: Published in the Proceedings of 30th The Web Conference (WWW 2021). Please cite the WWW version

Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

Authors: Mohammad Naseri, Jamie Hayes, Emiliano De Cristofaro

Abstract: Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local while only exchanging model updates. Alas, this is not necessarily free from privacy and robustness vulnerabilities, e.g., via membership, property, and backdoor attacks. This paper investigates whether and to what extent one can use differential Privacy (DP) to pro… ▽ More Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local while only exchanging model updates. Alas, this is not necessarily free from privacy and robustness vulnerabilities, e.g., via membership, property, and backdoor attacks. This paper investigates whether and to what extent one can use differential Privacy (DP) to protect both privacy and robustness in FL. To this end, we present a first-of-its-kind evaluation of Local and Central Differential Privacy (LDP/CDP) techniques in FL, assessing their feasibility and effectiveness. Our experiments show that both DP variants do d fend against backdoor attacks, albeit with varying levels of protection-utility trade-offs, but anyway more effectively than other robustness defenses. DP also mitigates white-box membership inference attacks in FL, and our work is the first to show it empirically. Neither LDP nor CDP, however, defend against property inference. Overall, our work provides a comprehensive, re-usable measurement methodology to quantify the trade-offs between robustness/privacy and utility in differentially private FL. △ Less

Submitted 27 May, 2022; v1 submitted 8 September, 2020; originally announced September 2020.

Journal ref: Published in the Proceedings of the 29th Network and Distributed System Security Symposium (NDSS 2022)

An Overview of Privacy in Machine Learning

Authors: Emiliano De Cristofaro

Abstract: Over the past few years, providers such as Google, Microsoft, and Amazon have started to provide customers with access to software interfaces allowing them to easily embed machine learning tasks into their applications. Overall, organizations can now use Machine Learning as a Service (MLaaS) engines to outsource complex tasks, e.g., training classifiers, performing predictions, clustering, etc. Th… ▽ More Over the past few years, providers such as Google, Microsoft, and Amazon have started to provide customers with access to software interfaces allowing them to easily embed machine learning tasks into their applications. Overall, organizations can now use Machine Learning as a Service (MLaaS) engines to outsource complex tasks, e.g., training classifiers, performing predictions, clustering, etc. They can also let others query models trained on their data. Naturally, this approach can also be used (and is often advocated) in other contexts, including government collaborations, citizen science projects, and business-to-business partnerships. However, if malicious users were able to recover data used to train these models, the resulting information leakage would create serious issues. Likewise, if the inner parameters of the model are considered proprietary information, then access to the model should not allow an adversary to learn such parameters. In this document, we set to review privacy challenges in this space, providing a systematic review of the relevant research literature, also exploring possible countermeasures. More specifically, we provide ample background information on relevant concepts around machine learning and privacy. Then, we discuss possible adversarial models and settings, cover a wide range of attacks that relate to private and/or sensitive information leakage, and review recent results attempting to defend against such attacks. Finally, we conclude with a list of open problems that require more work, including the need for better evaluations, more targeted defenses, and the study of the relation to policy and data protection efforts. △ Less

Submitted 18 May, 2020; originally announced May 2020.