Research › Usable Privacy

Usable Privacy and Security

Privacy protections only work if people can understand and use them. We study how people read privacy labels, how accessible privacy controls are to blind and low-vision users, and how to communicate formal guarantees like differential privacy to data practitioners and the public. Much of this work is in health contexts, where privacy and data-driven care meet.

Faculty

Yuanyuan Feng

Assistant Professor, Department of Computer Science

Selected publications

PoPETs 2026

"We Need a Standard": Toward an Expert-Informed Privacy Label for Differential Privacy

Onyinye Dibia, Mengyi Lu, Prianka Bhattacharjee, Joseph Near, Yuanyuan Feng

Proceedings on Privacy Enhancing Technologies (PoPETs), 2026(1)
PoPETs 2025

SoK: Usability Studies in Differential Privacy

Onyinye Dibia, Prianka Bhattacharjee, Brad Stenger, Steven Baldasty, Mako Bates, Ivoline Ngong, Yuanyuan Feng, Joseph Near

Proceedings on Privacy Enhancing Technologies (PoPETs), 2025(4):881–895
USENIX Security 2024

Understanding How to Inform Blind and Low-Vision Users About Data Privacy Through Privacy Question Answering Assistants

Yuanyuan Feng, A. Ravichander, Yuan Yao, Shikun Zhang, R. Chen, S. Wilson, Norman Sadeh

33rd USENIX Security Symposium
SOUPS 2024

Evaluating the Usability of Differential Privacy Tools with Data Practitioners

Ivoline Ngong, Brad Stenger, Joseph Near, Yuanyuan Feng

USENIX Symposium on Usable Privacy and Security (SOUPS)
JMIR 2024

Contextual Acceptance of COVID-19 Mitigation Mobile Apps in the United States: Mixed Methods Survey Study on Postpandemic Data Privacy

Yuanyuan Feng, Brad Stenger, Shikun Zhang

Journal of Medical Internet Research, 26:e57309
EuroS&P 2023

Exploring Smart Commercial Building Occupants' Perceptions and Notification Preferences of Internet of Things Data Collection in the United States

T. Le, A. Wang, Yuan Yao, Yuanyuan Feng, A. Heydarian, Norman Sadeh, Y. Tian

IEEE European Symposium on Security and Privacy (EuroS&P)
PoPETs 2022

How Usable Are iOS App Privacy Labels?

Shikun Zhang, Yuanyuan Feng, Yuan Yao, Lorrie Faith Cranor, Norman Sadeh

Proceedings on Privacy Enhancing Technologies (PoPETs), 2022(4):204–228
FAccT 2022

Stop the Spread: A Contextual Integrity Perspective on the Appropriateness of COVID-19 Vaccination Certificates

Shikun Zhang, Y. Shvartzshnaider, Yuanyuan Feng, H. Nissenbaum, Norman Sadeh

ACM Conference on Fairness, Accountability, and Transparency (FAccT), pp. 1657–1670
PoPETs 2021

Did You Know This Camera Tracks Your Mood? Modeling People's Privacy Expectations and Preferences in the Age of Video Analytics

Shikun Zhang, Yuanyuan Feng, Lujo Bauer, Lorrie Faith Cranor, Anupam Das, Norman Sadeh

Proceedings on Privacy Enhancing Technologies (PoPETs), 2021(2):282–304

🏅 12th Annual Privacy Papers for Policymakers Award
CHI 2021

A Design Space for Privacy Choices: Towards Meaningful Privacy Control in the Internet of Things

Yuanyuan Feng, Yuan Yao, Norman Sadeh

ACM Conference on Human Factors in Computing Systems (CHI)

All publications in this area →

Related courses

Course	Description	Instructor
CS 6550 Usable Privacy and Security Usable Privacy	Human factors in privacy and security: how people understand and use privacy controls, how to design and evaluate privacy-enhancing technologies, and methods from human-computer interaction for studying them.	Yuanyuan Feng

All security and privacy courses →

Funded projects

NSF

A Novel Mechanism for Effective and Accountable Communication of Differential Privacy through Multi-Stakeholder Research

SaTC: CORE: Small · CNS-2336550 · 2024–2027 · $599,994 · UVM PIs: Yuanyuan Feng, Joseph Near
NSF

A Contextual Integrity Approach for Privacy Reasoning Regarding Performance Tracking Technologies in US College Athletics

CRII: SaTC · CNS-2348294 · 2024–2026 · $175,000 · UVM PI: Yuanyuan Feng

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