About

Nick Feamster is a Neubauer Professor of Computer Science at the University of Chicago. His research focuses on areas within computer science that include security and privacy, data science, and the ethical implications of artificial intelligence. He is recognized for his impactful research and contributions to understanding and unmasking AI music, as well as addressing issues related to dark patterns in digital interfaces. As a distinguished faculty member, Feamster has received notable awards such as the 2026 Quantrell Teaching Award. His work often explores the intersection of technology, society, and policy, emphasizing the importance of ethical considerations in the development and deployment of computing systems. His academic and research pursuits aim to advance the understanding of complex issues in computer science, contributing to both theoretical foundations and practical applications.

Research topics

• Computer Science
• Internet privacy
• World Wide Web
• Computer Security
• Sociology
• Political Science
• Telecommunications
• Embedded system
• Data science
• Human–computer interaction
• Business
• Law
• Engineering
• Advertising

Selected publications

• Beyond PII: How Users Attempt to Estimate and Mitigate Implicit LLM Inference

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  Large Language Models (LLMs) such as ChatGPT can infer personal attributes from seemingly innocuous text, raising privacy risks beyond memorized data leakage. While prior work has demonstrated these risks, little is known about how users estimate and respond. We conducted a survey with 240 U.S. participants who judged text snippets for inference risks, reported concern levels, and attempted rewrites to block inference. We compared their rewrites with those generated by ChatGPT and Rescriber, a state-of-the-art sanitization tool. Results show that participants struggled to anticipate inference, performing a little better than chance. User rewrites were effective in just 28% of cases - better than Rescriber but worse than ChatGPT. We examined our participants’ rewriting strategies, and observed that while paraphrasing was the most common strategy it is also the least effective; instead abstraction and adding ambiguity were more successful. Our work highlights the importance of inference-aware design in LLM interactions.

  PublisherDOI

• Governance of AI-Generated Content: A Case Study on Social Media Platforms

  2026-04-13

  articleOpen access

  Online platforms are seeing increasing amounts of AI-generated content—text and other forms of media that are made or co-created with generative AI. This trend suggests platforms may need to establish governance frameworks, including policies and enforcement strategies for how users create, post, share, and engage with such content to encourage responsible use. We investigate the governance of AI-generated content across 40 popular social media platforms. Just over two-thirds explicitly describe governance of AI-generated content spanning six themes. Most platforms focus on moderating AI-generated content that violates established content rules and discloses AI-generated content. Fewer platforms—those that are focused on creativity and knowledge-sharing—address other issues such as ownership and monetization. Based on these findings, we suggest stakeholders and policymakers develop more direct, comprehensive, and forward-looking AI-generated content governance, as well as tools and education for users about the use of such content.

  PublisherDOI

• Governance of AI-Generated Content: A Case Study on Social Media Platforms

  arXiv (Cornell University) · 2026-03-08

  preprintOpen access

  Online platforms are seeing increasing amounts of AI-generated content -- text and other forms of media that are made or co-created with generative AI. This trend suggests platforms may need to establish governance frameworks, including policies and enforcement strategies for how users create, post, share, and engage with such content to encourage responsible use. We investigate the governance of AI-generated content across 40 popular social media platforms. Just over two-thirds explicitly describe governance of AI-generated content spanning six themes. Most platforms focus on moderating AI-generated content that violates established content rules and discloses AI-generated content. Fewer platforms -- those that are focused on creativity and knowledge-sharing -- address other issues such as ownership and monetization. Based on these findings, we suggest stakeholders and policymakers develop more direct, comprehensive, and forward-looking AI-generated content governance, as well as tools and education for users about the use of such content.

  PublisherDOI

• Deep canvassing with automated conversational agents: Personalized messaging to change attitudes

  Research & Politics · 2026-01-01

  articleOpen access

  We test a social media conversational agent for canvassing on the topic of anti-transgender prejudice, replicating and benchmarking treatment effects. In-person deep canvassing is the gold standard for durably changing attitudes on polarizing topics. However, door-to-door canvassing is costly, and many populations may not be feasibly reached in this manner. Campaigns are already conducting outreach using digital tools, including text messages and social media. If appropriately trained agents messaging over social media can achieve a fraction of the effect of in-person canvassing, canvassing may be scaled up to achieve large overall impacts at lower costs. Scripts used in this application are based on those used by transgender allies in the original study. To personalize messaging, the conversational agent uses natural language processing to detect conversational topics, and shares relevant pre-scripted messages of information and third-person experiences, encouraging respondents to engage in perspective-taking with respect to an outgroup. This study demonstrates the potential of automated social media messaging for deep canvassing, with possible applications by governments, public health agencies, and political organizations. Estimated effects are positive and significant under covariate adjustment and reweighting; due to important differential attrition, partial-identification bounds are also reported and include zero.

  PublisherDOI

• NetSSM: Multi-Flow and State-Aware Network Trace Generation using State-Space Models

  Proceedings of the ACM on Networking · 2026-03-25

  articleOpen accessSenior author

  Access to raw network traffic data is essential for many computer networking tasks, from traffic modeling to performance evaluation. Unfortunately, this data is scarce due to high collection costs and governance rules. and lack robust evaluations tied to real-world utility. We propose a new method based on state space models called NetSSM that generates raw network traffic at the packet-level granularity. Our approach captures interactions between multiple, interleaved flows -- an objective unexplored in prior work -- and effectively reasons about flow-state in sessions to capture traffic characteristics. NetSSM accomplishes this by training with a context window more than 8× longer, and produces traces up to 78× longer than existing transformer-based raw packet generators. compliance with standard protocol requirements and flow and session-level traffic characteristics.

  PublisherDOI

• Measuring Low Latency at Scale: A Field Study of L4S in Residential Broadband

  Lecture notes in computer science · 2026-01-01

  book-chapterSenior author
  PublisherDOI

• WiFinger: Fingerprinting Noisy IoT Event Traffic Using Packet-level Sequence Matching

  2026-01-01

  articleOpen accessSenior author

  requirement on attackers [10], who must either exploit the LAN access point or obtain authorized access within the ISP's network (WAN).In contrast, since most IoT devices connect wirelessly (Wi-Fi in this work), their traffic can be easily sniffed using a wireless adapter, significantly lowering the barrier and increasing the practicality of such attacks.Given that network event classification has been studied for decades, an intuitive question arises: can these established solutions simply work for Wi-Fi layer attacks?Unfortunately, our findings indicate the opposite.By applying prevalent methods to IoT traffic at the Wi-Fi layer, we have identified significant limitations and challenges of these solutions in tuning model performance and reducing manual efforts, as detailed in Table I. Methods Features Data VolAbstract-IoT environments such as smart homes are susceptible to privacy inference attacks, where attackers can analyze patterns of encrypted network traffic to infer the state of devices and even the activities of people.While most existing attacks exploit ML techniques for discovering such traffic patterns, they underperform on wireless traffic, especially Wi-Fi, due to its heavy noisiness and the packet loss of wireless sniffing.In addition, these approaches commonly target distinguishing chunked IoT event traffic samples, and they fail at effectively tracking multiple events simultaneously.In this work, we propose WiFinger, a finegrained multi-IoT event fingerprinting approach against noisy traffic.WiFinger turns the traffic pattern classification task into a subsequence matching problem and introduces novel techniques to account for the high time complexity while maintaining high accuracy.In addition, its reliance on training sample volumes reduces efforts for any future fingerprint updates.Experiments demonstrate that WiFinger outperforms existing approaches under practical threat models, with an average recall of 89% (v.s.49% and 46% respectively) and almost zero false positives for various IoT events.

  PublisherDOI

• Poster: Global Measurements of the Availability and Response Times of Public Encrypted DNS Resolvers

  2025-10-28

  articleSenior author

  Previous studies have measured encrypted DNS performance, but they have mostly focused on mainstream DNS resolvers [4, 6, 7]. We expand on previous studies, exploring the performance of all encrypted DNS resolvers—from a variety of global vantage points, as opposed to simply characterizing the mainstream DoH providers from well-connected vantage points. Our goal is to compare the performance of encrypted DNS resolvers to each other to understand the extent to which this larger set of DNS resolvers could be used by clients and applications in different regions.

  PublisherDOI

• "I Cannot Write This Because It Violates Our Content Policy": Understanding Content Moderation Policies and User Experiences in Generative AI Products

  ArXiv.org · 2025-06-16

  preprintOpen access

  While recent research has focused on developing safeguards for generative AI (GAI) model-level content safety, little is known about how content moderation to prevent malicious content performs for end-users in real-world GAI products. To bridge this gap, we investigated content moderation policies and their enforcement in GAI online tools -- consumer-facing web-based GAI applications. We first analyzed content moderation policies of 14 GAI online tools. While these policies are comprehensive in outlining moderation practices, they usually lack details on practical implementations and are not specific about how users can aid in moderation or appeal moderation decisions. Next, we examined user-experienced content moderation successes and failures through Reddit discussions on GAI online tools. We found that although moderation systems succeeded in blocking malicious generations pervasively, users frequently experienced frustration in failures of both moderation systems and user support after moderation. Based on these findings, we suggest improvements for content moderation policy and user experiences in real-world GAI products.

  PublisherOA PDFDOI

• Towards Scalable Defenses against Intimate Partner Infiltrations

  ArXiv.org · 2025-02-06

  preprintOpen access

  Intimate Partner Infiltration (IPI)--a type of Intimate Partner Violence (IPV) that typically requires physical access to a victim's device--is a pervasive concern around the world, often manifesting through digital surveillance, control, and monitoring. Unlike conventional cyberattacks, IPI perpetrators leverage close proximity and personal knowledge to circumvent standard protections, underscoring the need for targeted interventions. While security clinics and other human-centered approaches effectively tailor solutions for victims, their scalability remains constrained by resource limitations and the need for specialized counseling. We present AID, an Automated IPI Detection system that continuously monitors for unauthorized access and suspicious behaviors on smartphones. AID employs a unified architecture to process multimodal signals stealthily and preserve user privacy. A brief calibration phase upon installation enables AID to adapt to each user's behavioral patterns, achieving high accuracy with minimal false alarms. Our 27-participant user study demonstrates that AID achieves highly accurate detection of non-owner access and fine-grained IPI-related activities, attaining a false positive rate of 1.6%, which is 11x lower than existing methods, and an end-to-end F1 score of 0.981. These findings suggest that AID can serve as a forensic tool that security clinics can deploy to scale their ability to identify IPI tactics and deliver personalized, far-reaching support to survivors.

  PublisherOA PDFDOI

Recent grants

• FIA: Collaborative Research: Architecting for Innovation

  NSF · $200k · 2010–2014

• NeTS: Medium: Collaborative Research: Optimizing Network Support for Cloud Services: From Short-Term Measurements to Long-Term Planning

  NSF · $400k · 2015–2017

• RAPID: Measuring the Effects of the COVID-19 Pandemic on Broadband Access Networks to Inform Robust Network Design

  NSF · $187k · 2020–2023

• TC: Large: Collaborative Research: Facilitating Free and Open Access to Information on the Internet

  NSF · $1.2M · 2015–2018

• NeTS: Medium: Collaborative Research: A Software Defined Internet Exchange

  NSF · $500k · 2015–2018

Frequent coauthors

• Renata Teixeira

  Netflix (United States)

  61 shared

• Paul Schmitt

  University of Hawaiʻi at Mānoa

  59 shared

• Francesco Bronzino

  52 shared

• Jennifer Rexford

  47 shared

• Srikanth Sundaresan

  Menlo School

  36 shared

• Vytautas Valancius

  Google (United States)

  35 shared

• Hari Balakrishnan

  IIT@MIT

  32 shared

• Marshini Chetty

  University of Chicago

  31 shared

Labs

• Research Labs & GroupsPI

Education

• Ph.D., Computer Science

  MIT

  2005

• Other, Electrical Engineering and Computer Science

  MIT

  2001

• Other, Electrical Engineering and Computer Science

  MIT

  2000

Awards & honors

• ACM Fellow
• USENIX Community Contribution Award, USENIX/ACM Symposium on…
• USENIX “Test of Time” Best Paper Award
• Internet Research Task Force Applied Networking Research Pri…
• ACM SIGCOMM Community Award