:Index / 6 publications
Usable security and privacy through an HCI lens — how real people adopt privacy controls, weigh privacy against money, assign blame after breaches, and decide to update their software.
R01
★ Best Paper Award — ACM CHI 2023
As tech companies increasingly collect user data, understanding how users perceive and respond to privacy controls is crucial. This study examines the trust gap between technical and non-technical users in service providers' data collection — informing privacy tools that resonate with diverse user groups.
R02
As the Internet of Things expands, so do concerns about personal data privacy. Our research reveals users are willing to pay for enhanced privacy protection — especially those with higher technical literacy — underscoring the need for transparent, customizable privacy settings in IoT devices.
R03
This study explores how users' past security actions impact their blame attribution. Surprisingly, non-compliant users tend to deflect blame onto service providers — insight that can shape more effective pre- and post-breach communication strategies.
R04
As the 'pay for privacy' model gains momentum, we investigated how users weigh data privacy against monetary benefits — using an incentive-compatible lottery on Prolific to uncover the subtle calculations driving privacy-focused IoT purchases.
R05
Why are some users more likely to update software than others? Update type and required restarts were not significant — but trust in the provider was. Intriguingly, users intending to update often experienced higher cognitive dissonance, suggesting updating is a complex psychological process.
R06 — Undergraduate thesis
Anime face detection poses unique challenges due to its stylized nature. We trained a cascade of classifiers, each focused on a specific facial feature, achieving high detection accuracy.
