EDBT 2026 Demo / reviewers in the wild / expert
Jonathan R. Mayer
dblp:116/8542
· DBLP profile ↗
15ranked-venue papers
1as first author
11since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 9 · 1 first-author · 8 since 2021Computer networks · 2 · 1 since 2021Databases, data management, data science and information retrieval · 2 · 1 since 2021Human-computer interaction and ubiquitous computing · 2 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | WireWatch: Measuring the Security of Proprietary Network Encryption in the Global Android EcosystemabstractWe present WireWatch, a large-scale measurement pipeline to evaluate the network security of Android apps. WireWatch measures apps' usage of plaintext network traffic and non-standard, proprietary network cryptography. We found that 47.6% of top Mi Store applications used proprietary network cryptography without any additional encryption, compared to only 3.51% of top Google Play Store applications. We analyzed the 18 most popular protocols from WireWatch, which belonged to 9 protocol families, including cryptosystems designed by Alibaba, iQIYI, Kuaishou, and Tencent. We found that 8 of these protocol families sent requests that allowed network eavesdroppers to decrypt underlying data, including browsing data and device metadata, among various other issues, such as being downgradable, not validating TLS certificates, and the use of RSA without OAEP. These vulnerabilities affected 26.9% of our Mi Store dataset with a cumulative 130 billion downloads. Ultimately, WireWatch reveals that a large portion of massively popular applications are using insecure proprietary network protocols to encrypt sensitive user data. Mona Wang, Jeffrey Knockel, Zoë Reichert, Prateek Mittal, Jonathan R. Mayer |
SP | 5 |
| 2025 | SoK: Machine Learning for Misinformation Detection
Madelyne Xiao, Jonathan R. Mayer |
USENIX Security Symposium | 2 |
| 2023 | Public Verification for Private Hash MatchingabstractEnd-to-end encryption (E2EE) prevents online services from accessing user content. This important security property is also an obstacle for content moderation methods that involve content analysis. The tension between E2EE and efforts to combat child sexual abuse material (CSAM) has become a global flashpoint in encryption policy, because the predominant method of detecting harmful content—server-side perceptual hash matching on plaintext images—is unavailable.Recent applied cryptography advances enable private hash matching (PHM), where a service can match user content against a set of known CSAM images without revealing the hash set to users or nonmatching content to the service. These designs, especially a 2021 proposal for identifying CSAM in Apple’s iCloud Photos service, have attracted widespread criticism for creating risks to security, privacy, and free expression.In this work, we aim to advance scholarship and dialogue about PHM by contributing new cryptographic methods for system verification by the general public. We begin with motivation, describing the rationale for PHM to detect CSAM and the serious societal and technical issues with its deployment. Verification could partially address shortcomings of PHM, and we systematize critiques into two areas for auditing: trust in the hash set and trust in the implementation. We explain how, while these two issues cannot be fully resolved by technology alone, there are possible cryptographic trust improvements.The central contributions of this paper are novel cryptographic protocols that enable three types of public verification for PHM systems: (1) certification that external groups approve the hash set, (2) proof that particular lawful content is not in the hash set, and (3) eventual notification to users of false positive matches. The protocols that we describe are practical, efficient, and compatible with existing PHM constructions. Sarah Scheffler, Anunay Kulshrestha, Jonathan R. Mayer |
SP | 3 |
| 2023 | Account Verification on Social Media: User Perceptions and Paid Enrollment
Madelyne Xiao, Mona Wang, Anunay Kulshrestha, Jonathan R. Mayer |
USENIX Security Symposium | 4 |
| 2023 | SoK: Content Moderation for End-to-End EncryptionabstractPopular messaging applications now enable end-to-end-encryption (E2EE) by default, and E2EE data storage is becoming common. These important advances for security and privacy create new content moderation challenges for online services, because services can no longer directly access plaintext content. While ongoing public policy debates about E2EE and content moderation in the United States and European Union emphasize child sexual abuse material and misinformation in messaging and storage, we identify and synthesize a wealth of scholarship that goes far beyond those topics. We bridge literature that is diverse in both content moderation subject matter, such as malware, spam, hate speech, terrorist content, and enterprise policy compliance, as well as intended deployments, including not only privacy-preserving content moderation for messaging, email, and cloud storage, but also private introspection of encrypted web traffic by middleboxes. In this work, we systematize the study of content moderation in E2EE settings. We set out a process pipeline for content moderation, drawing on a broad interdisciplinary literature that is not specific to E2EE. We examine cryptography and policy design choices at all stages of this pipeline, and we suggest areas of future research to fill gaps in literature and better understand possible paths forward. Sarah Scheffler, Jonathan R. Mayer |
Proc. Priv. Enhancing Technol. | 2 |
| 2022 | Network measurement methods for locating and examining censorship devicesabstractAdvances in networking and firewall technology have led to the emergence of network censorship devices that can perform large-scale, highly-performant content blocking. While such devices have proliferated, techniques to locate, identify, and understand them are still limited, require cumbersome manual effort, and are developed on a case-by-case basis. Ram Sundara Raman, Mona Wang, Jakub Dalek, Jonathan R. Mayer, Roya Ensafi |
CoNEXT | 4 |
| 2022 | Estimating Incidental Collection in Foreign Intelligence Surveillance: Large-Scale Multiparty Private Set Intersection with Union and Sum
Anunay Kulshrestha, Jonathan R. Mayer |
USENIX Security Symposium | 2 |
| 2021 | What Makes a Dark Pattern... Dark?: Design Attributes, Normative Considerations, and Measurement MethodsabstractThere is a rapidly growing literature on dark patterns, user interface designs—typically related to shopping or privacy—that researchers deem problematic. Recent work has been predominantly descriptive, documenting and categorizing objectionable user interfaces. These contributions have been invaluable in highlighting specific designs for researchers and policymakers. But the current literature lacks a conceptual foundation: What makes a user interface a dark pattern? Why are certain designs problematic for users or society? Arunesh Mathur, Mihir Kshirsagar, Jonathan R. Mayer |
CHI | 3 |
| 2021 | Adapting Security Warnings to Counter Online Disinformation
Jerry Wei, Eli Lucherini, J. Nathan Matias, Jonathan R. Mayer |
USENIX Security Symposium | 6 |
| 2021 | Identifying Harmful Media in End-to-End Encrypted Communication: Efficient Private Membership Computation
Anunay Kulshrestha, Jonathan R. Mayer |
USENIX Security Symposium | 2 |
| 2021 | Privacy Policies over Time: Curation and Analysis of a Million-Document DatasetabstractAutomated analysis of privacy policies has proved a fruitful research direction, with developments such as automated policy summarization, question answering systems, and compliance detection. Prior research has been limited to analysis of privacy policies from a single point in time or from short spans of time, as researchers did not have access to a large-scale, longitudinal, curated dataset. To address this gap, we developed a crawler that discovers, downloads, and extracts archived privacy policies from the Internet Archive's Wayback Machine. Using the crawler and following a series of validation and quality control steps, we curated a dataset of 1,071,488 English language privacy policies, spanning over two decades and over 130,000 distinct websites. Our analyses of the data paint a troubling picture of the transparency and accessibility of privacy policies. By comparing the occurrence of tracking-related terminology in our dataset to prior web privacy measurements, we find that privacy policies have consistently failed to disclose the presence of common tracking technologies and third parties. We also find that over the last twenty years privacy policies have become even more difficult to read, doubling in length and increasing a full grade in the median reading level. Our data indicate that self-regulation for first-party websites has stagnated, while self-regulation for third parties has increased but is dominated by online advertising trade associations. Finally, we contribute to the literature on privacy regulation by demonstrating the historic impact of the GDPR on privacy policies. Ryan Amos, Gunes Acar, Eli Lucherini, Mihir Kshirsagar, Arvind Narayanan, Jonathan R. Mayer |
WWW | 6 |
| 2020 | No WAN's Land: Mapping U.S. Broadband Coverage with Millions of Address Queries to ISPsabstractAccurate broadband coverage data is essential for public policy planning and government support programs. In the United States, the Federal Communications Commission is responsible for maintaining national broadband coverage data. Observers have panned the FCC's broadband maps for overstating availability, due to coarsegrained data collection and a low coverage threshold. David J. Major, Ross Teixeira, Jonathan R. Mayer |
Internet Measurement Conference | 3 |
| 2019 | Dark Patterns at Scale: Findings from a Crawl of 11K Shopping WebsitesabstractDark patterns are user interface design choices that benefit an online service by coercing, steering, or deceiving users into making unintended and potentially harmful decisions. We present automated techniques that enable experts to identify dark patterns on a large set of websites. Using these techniques, we study shopping websites, which often use dark patterns to influence users into making more purchases or disclosing more information than they would otherwise. Analyzing ~53K product pages from ~11K shopping websites, we discover 1,818 dark pattern instances, together representing 15 types and 7 broader categories. We examine these dark patterns for deceptive practices, and find 183 websites that engage in such practices. We also uncover 22 third-party entities that offer dark patterns as a turnkey solution. Finally, we develop a taxonomy of dark pattern characteristics that describes the underlying influence of the dark patterns and their potential harm on user decision-making. Based on our findings, we make recommendations for stakeholders including researchers and regulators to study, mitigate, and minimize the use of these patterns. Arunesh Mathur, Gunes Acar, Michael Friedman, Eli Lucherini, Jonathan R. Mayer, Marshini Chetty, Arvind Narayanan |
Proc. ACM Hum. Comput. Interact. | 5 |
| 2015 | Cookies That Give You Away: The Surveillance Implications of Web TrackingabstractWe study the ability of a passive eavesdropper to leverage "third-party" HTTP tracking cookies for mass surveillance. If two web pages embed the same tracker which tags the browser with a unique cookie, then the adversary can link visits to those pages from the same user (i.e., browser instance) even if the user's IP address varies. Further, many popular websites leak a logged-in user's identity to an eavesdropper in unencrypted traffic. To evaluate the effectiveness of our attack, we introduce a methodology that combines web measurement and network measurement. Using OpenWPM, our web privacy measurement platform, we simulate users browsing the web and find that the adversary can reconstruct 62-73% of a typical user's browsing history. We then analyze the effect of the physical location of the wiretap as well as legal restrictions such as the NSA's "one-end foreign" rule. Using measurement units in various locations - Asia, Europe, and the United States - we show that foreign users are highly vulnerable to the NSA's dragnet surveillance due to the concentration of third-party trackers in the U.S. Finally, we find that some browser-based privacy tools mitigate the attack while others are largely ineffective. Steven Englehardt, Dillon Reisman, Christian Eubank, Peter Zimmerman, Jonathan R. Mayer, Arvind Narayanan, Edward W. Felten |
WWW | 5 |
| 2012 | Third-Party Web Tracking: Policy and TechnologyabstractIn the early days of the web, content was designed and hosted by a single person, group, or organization. No longer. Webpages are increasingly composed of content from myriad unrelated "third-party" websites in the business of advertising, analytics, social networking, and more. Third-party services have tremendous value: they support free content and facilitate web innovation. But third-party services come at a privacy cost: researchers, civil society organizations, and policymakers have increasingly called attention to how third parties can track a user's browsing activities across websites. This paper surveys the current policy debate surrounding third-party web tracking and explains the relevant technology. It also presents the FourthParty web measurement platform and studies we have conducted with it. Our aim is to inform researchers with essential background and tools for contributing to public understanding and policy debates about web tracking. Jonathan R. Mayer, John C. Mitchell |
IEEE Symposium on Security and Privacy | 1 |