Visual Privacy Protection (VPP) systems, such as blurring, pixelation, adversarial perturbations, and face replacement, are increasingly used to prevent identity disclosure in surveillance footage and publicly shared datasets. However, little is known about whether these protections apply equally across demographic groups, and uneven protection may create exploitable vulnerabilities. In this paper, we introduce FairDeFace, the first comprehensive framework for auditing the fairness, robustness, and visual quality of VPP systems under realistic adversarial threat models. Our framework evaluates how well different VPP techniques resist recognition attacks while preserving facial attributes and visual consistency across groups defined by gender and skin tone. It integrates benchmark datasets, face recognition models, attack pipelines, fairness measures, and novel visualization and statistical tools to trace where and why protections fail—for example, by identifying facial regions that are inconsistently altered. Across 500+ experiments involving seven state-of-the-art VPP methods, we uncover substantial disparities in protection strength and image quality. Certain groups, particularly Black males and females, receive consistently lower protection, framing fairness not only as an ethical concern but as a critical security and privacy risk. We release FairDeFace as an open and extensible framework to support the development and evaluation of equitable visual privacy systems.

Auditing is critical to ensuring the fairness and reliability of decision-making systems. However, auditing a black-box system for bias can be challenging due to the lack of transparency in the model’s internal workings. In many web applications, such as Yelp, it is challenging, if not impossible, to manipulate their inputs systematically to identify bias in the output. Yelp connects users and businesses, where users identify new businesses and simultaneously express their experiences through reviews. Yelp recommendation software moderates user-provided content by categorizing it into recommended and not-recommended sections. The recommended reviews, among other attributes, are used by Yelp’s ranking algorithm to rank businesses in a neighborhood. Due to Yelp’s substantial popularity and its high impact on local businesses’ success, understanding the bias of its algorithms is crucial. This data-driven study, for the first time, investigates the bias of Yelp’s business ranking and review recommendation system. We examine three hypotheses to assess if Yelp’s recommendation software shows bias against reviews of less established users with fewer friends and reviews and if Yelp’s business ranking algorithm shows bias against restaurants located in specific neighborhoods, particularly in hotspot regions, with specific demographic compositions. Our findings show that reviews of less-established users are disproportionately categorized as not-recommended. We also find a positive association between restaurants’ location in hotspot regions and their average exposure. Furthermore, we observed some cases of severe disparity bias in cities where the hotspots are in neighborhoods with less demographic diversity or higher affluence and education levels.

During periods of strict internet censorship, maintaining access to online information and communication becomes paramount. However, users must often navigate complicated pathways to find effective censorship circumvention technologies (CCTs). Utilizing real-time data from over 50M posts collected from Twitter and Telegram from September 18th, 2022, to January 31st, 2023, during a peak period of censorship, we examined the impact of CCTs, such as VPNs, proxies, and alternative connectivity solutions, on digital rights, privacy, and internet governance. Through a mixed-method analysis, our findings reveal user resilience and adaptability when the community collaboratively shares and discusses knowledge and resources. First, we developed a codebook for discussions considering English and, for the first time, Persian posts, highlighting the main problems users encounter when attempting to bypass the internet restrictions. Several concerns were common across these discourses, such as traceability, identifiability, and accidental use of malicious configurations. Our temporal study, conducted over 20 weeks, showed shifts in VPN preferences due to changing censorship strategies, with the inclusion of more privacy-focused and accessibility features leading to higher adoption. We also found several dedicated popular VPN channels that shared malicious files masked as free VPN services.

Social media platforms employ various content moderation techniques to remove harmful, offensive, and toxic content, with moderation levels varying across platforms and evolving over time. Parler, a fringe platform popular among conservative users, initially had minimal moderation, promoting itself as a space for open discussion. However, in 2021, it was removed from the Apple and Google App Stores and suspended from Amazon Web Services due to inadequate moderation of harmful content. After a month-long suspension, Parler returned with stricter guidelines, offering a unique opportunity to study the impact of platform-wide policy changes on user behavior and content outcomes. In this paper, we analyzed Parler data to assess the causal associations of these moderation changes on content toxicity and factuality. Using a longitudinal dataset of 17M posts from 432K users, who were active both before and after replatforming, we employed quasi-experimental analysis, controlling for confounding factors. We introduced a novel approach by using data from another social media platform, Twitter, to account for a critical confounding factor: offline events. This allowed us to isolate the effects of Parler's replatforming policies from external real-world influences. Our findings demonstrate that Parler's moderation changes are causally associated with a significant reduction in all forms of toxicity (p < 0.001). Additionally, we observed an increase in the factuality of the news sites shared and a reduction in the number of conspiracy/ pseudoscience sources.

Social media platforms have been establishing content moderation guidelines and employing various moderation policies to counter hate speech and misinformation. The goal of this paper is to study these community guidelines and moderation practices, as well as the relevant research publications, to identify the research gaps, differences in moderation techniques, and challenges that should be tackled by the social media platforms and the research community. To this end, we study and analyze fourteen most popular social media content moderation guidelines and practices, and consolidate them. We then introduce three taxonomies drawn from this analysis as well as covering over two hundred interdisciplinary research papers about moderation strategies. We identify the differences between the content moderation employed in mainstream and fringe social media platforms. Finally, we have in-depth applied discussions on both research and practical challenges and solutions.

Prior work has extensively studied misinformation related to news, politics, and health, however, misinformation can also be about technological topics. While less controversial, such misinformation can severely impact companies’ reputations and revenues, and users’ online experiences. Recently, social media has also been increasingly used as a novel source of knowledgebase for extracting timely and relevant security threats, which are fed to the threat intelligence systems for better performance. However, with possible campaigns spreading false security threats, these systems can become vulnerable to poisoning attacks. In this work, we proposed novel approaches for detecting misinformation about cybersecurity and privacy threats on social media, focusing on two topics with different types of misinformation: phishing websites and Zoom’s security & privacy threats. We developed a framework for detecting inaccurate phishing claims on Twitter. Using this framework, we could label about 9% of URLs and 22% of phishing reports as misinformation. We also proposed another framework for detecting misinformation related to Zoom’s security and privacy threats on multiple platforms. Our classifiers showed great performance with more than 98% accuracy. Employing these classifiers on the posts from Facebook, Instagram, Reddit, and Twitter, we found respectively that about 18%, 3%, 4%, and 3% of posts were misinformation. In addition, we studied the characteristics of misinformation posts, their authors, and their timelines, which helped us identify campaigns.