Ph.D. Candidate · University of Chicago
tranv@uchicago.edu
Welcome! I'm Van Tran, a Ph.D. candidate at the University of Chicago, advised by Prof. Nick Feamster in the NOISE Lab.
My research lies at the intersection of machine learning, cybersecurity, and privacy, with a focus on leveraging AI to address real-world security and privacy challenges while making AI systems themselves more secure and trustworthy.
Research Interests
- AI for Security and Privacy: Developing scalable, ML-driven techniques to detect cybersecurity threats, Internet censorship, and violations of privacy regulations, including the CCPA.
- Security and Privacy of Generative AI: Investigating privacy vulnerabilities in generative models, including membership inference, data extraction, and sensitive information leakage, and evaluating the effectiveness of privacy defenses.
- AI-Driven Cyber Threats: Measuring and characterizing the growing use of LLMs in malicious activities, particularly phishing and spam.
- Secure and Privacy-Preserving AI Agents: Designing fine-grained authorization and access control mechanisms to protect sensitive resources and prevent unauthorized actions in autonomous AI systems, as well as context-aware privacy safeguards to prevent sensitive information leakage in AI agents.
Selected Publications
Quantifying the privacy implications of high-fidelity synthetic network traffic
ACM Internet Measurement Conference, 2026
Layered, Overlapping, and Inconsistent: A Large-Scale Analysis of the Multiple Privacy Policies and Controls of US Banks
ACM Conference on Computer and Communications Security (CCS), 2025
Distinguished Paper Award
Do spammers dream of electric sheep? Characterizing the prevalence of LLM-generated malicious emails
ACM Internet Measurement Conference, 2025
Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning
ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD), 2023
Measuring and evading Turkmenistan's internet censorship: A case study in large-scale measurements of a low-penetration country
ACM Web Conference (WWW), 2023
Dark Patterns in the Opt-Out Process and Compliance with the California Consumer Privacy Act (CCPA)
ACM CHI Conference on Human Factors in Computing Systems, 2025
Measuring Compliance with the California Consumer Privacy Act Over Space and Time
ACM CHI Conference on Human Factors in Computing Systems, 2024
MYCROFT: Towards Effective and Efficient External Data Augmentation
Workshop on ML for Systems at NeurIPS, 2025
Professional Service
- Reviewer — International Conference on Learning Representations (ICLR) 2025
- Reviewer — ACM CHI Conference on Human Factors in Computing Systems, 2025
Awards & Achievements
- Crerar Fellowship, University of Chicago, 2021
- Contextual Integrity (CI) Travel Grant ($1,500), PrivaCI, 2023
- Award for Excellence in Computer Science, Colgate University, 2021 — Awarded to top 5 CS seniors for excellence in academics and research
- Charles A. Dana Scholar, Colgate University, 2020 — Awarded to top 20 students with GPA ≥ 4.0
- Leadership Excellence Award, Colgate University