
Eric Zeng
Postdoctoral Fellow
Massive Data Institute
McCourt School of Public Policy
Georgetown University
Contact
About Me
Hello! I am a computer security and privacy researcher, and I study security, privacy, and safety harms that people experience on online platforms.
I am currently a postdoctoral fellow at the Massive Data Institute at Georgetown University, where I work with Professor Elissa Redmiles. Previously, I was a postdoctoral researcher at Carnegie Mellon University CyLab, where I was advised by Professor Lujo Bauer. I graduated with a PhD in Computer Science & Engineering from the University of Washington, where I was advised by Professor Franzi Roesner, and I was part of the Security and Privacy Lab.
My current research investigates safety risks from AI-generated images, social media recommendation algorithms, and deceptive online advertising. I combine methods and frameworks from computer security, human-computer interaction, psychology, and the social sciences to characterize and quantify the impact of these technologies on users.
I also aim to share my research tools to improve reproducibility and to enable researchers to build on our work. I currently maintain adscraper, a tool for scraping ads from the web, which we used to collect datasets on health-related online advertising, deceptive political ads during the 2020 U.S. elections, and user perceptions of problematic advertising. Tools I've made available from past projects include an in-browser ad measurement tool, proximity-based access controls in smart homes, and a Keybase-powered encrypted email client.
Selected Publications
- Measuring Risks to Users' Health Privacy Posed by Third-Party Web Tracking and Targeted Advertising (CHI 2025)
- Anti-Privacy and Anti-Security Advice on TikTok: Case Studies of Technology-Enabled Surveillance and Control in Intimate Partner and Parent-Child Relationships (SOUPS 2022)
- Polls, Clickbait, and Commemorative $2 Bills: Problematic Political Advertising on News and Media Websites Around the 2020 U.S. Elections (IMC 2021)
- What Makes a "Bad" Ad? User Perceptions of Problematic Online Advertising (CHI 2021)
- Understanding and Improving Security and Privacy in Multi-User Smart Homes: A Design Exploration and In-Home User Study (USENIX Security 2019)