UW Tacoma
User ASPECT Lab

The User ASPECT Lab advocates for user autonomy in managing information security and privacy in today’s complex cyberspace. While automation technologies increasingly support digital safety, we believe that users should retain the fundamental right to understand and actively manage their own protective measures. Our research advances this principle by designing evidence-based interactive interventions, such as educational video games, explanatory visualizations, and AI assistants, so that users can make informed decisions about sharing their personal information and protect themselves. We welcome students with interdisciplinary backgrounds in information security and social psychology to join us.

Dr. Wen is an Assistant Professor of Information Technology at the University of Washington Tacoma School of Engineering & Technology. He leads human-computer interaction research, focusing on usable privacy and security. He is passionate about enhancing information technologies to better align with human values, limitations, and needs. In his lab, fixing gaps in user autonomy, security, and privacy is like recharging core energy bars that fuel every mission toward a safer cyberspace.

1. Computational Approaches to Fraud Vulnerability and User Protection

Abstract

We have been investigating computational models to detect and reduce user vulnerability to online frauds and scams. Our human-centered approach integrates behavioral modeling, predictive machine learning, and interactive training. This research contributes to designing accountable information systems that support rather than replace human judgment in cyberspace interactions.

Selective Publications

1. What.Hack Anti-Phishing Training Game (Demo)
2. Families’ Vision of Generative AI Agents for Household Safety Against Digital and Physical Threats (ACM CSCW, 2025)
3. Email Reading Behavior-Informed Machine Learning Model to Predict Phishing Susceptibility (AIS&P, 2023)

2. Balancing Individual Privacy and Collective Utility in Information Systems

Abstract

We have been investigating transparency challenges when organizations adopt privacy-enhancing technologies for applications such as AI training and financial transactions. Our research examines how privacy guarantees can be effectively verified and communicated to stakeholders. This research contributes to designing accountable information systems where informed decisions about privacy-utility tradeoffs are possible.

Selective Publications

1. Local Differential Privacy Visual Explainer (Demo)
2. Side-Channel Attacks and New Principles in the Shuffle Model of Differential Privacy (IEEE TIFS, 2025)
3. The Influence of Explanation Designs on User Understanding Differential Privacy and Making Data-Sharing Decision (Information Sciences, 2023)