17 citations · 18 across the 6 of their papers we have counts for
6 papers
"You do understand that people don't trust technology?": Explaining Trusted Execution Environments to Non-Experts
McKenna McCall, Carolina Carreira, Miguel Flores +1
Trusted Execution Environments (TEEs) protect confidentiality and integrity of trusted applications by creating an isolated environment for executing code. Prior work has shown tha…
What People See (and Miss) About Generative AI Risks: Perceptions of Failures, Risks, and Who Should Address Them
Megan Li, Wendy Bickersteth, Ningjing Tang +7
Despite growing concerns about the risks of Generative AI (GenAI), there is limited understanding of public perceptions of these risks and their associated failure modes -- defined…
Design and Evaluation of Privacy-Preserving Protocols for Agent-Facilitated Mobile Money Services in Kenya
Karen Sowon, Collins W. Munyendo, Lily Klucinec +6
Mobile Money (MoMo), a technology that allows users to complete financial transactions using a mobile phone without requiring a bank account, is a common method for processing fina…
Recruiting Teenage Participants for an Online Security Experiment: A Case Study Using Peachjar
Elijah Bouma-Sims, Lily Klucinec, Mandy Lanyon +2
The recruitment of teenagers for usable privacy and security research is challenging, but essential. This case study presents our experience using the online flier distribution ser…
Matcha: An IDE Plugin for Creating Accurate Privacy Nutrition Labels
Tianshi Li, Lorrie Faith Cranor, Yuvraj Agarwal +1
Apple and Google introduced their versions of privacy nutrition labels to the mobile app stores to better inform users of the apps' data practices. However, these labels are self-r…
Data Safety vs. App Privacy: Comparing the Usability of Android and iOS Privacy Labels
Yanzi Lin, Jaideep Juneja, Eleanor Birrell +1
Privacy labels -- standardized, compact representations of data collection and data use practices -- are often presented as a solution to the shortcomings of privacy policies. Appl…