5 papers
Security and Privacy Prompts in the Wild: What Users Ask LLMs and How LLMs Respond
Hobin Kim, Xiaoyuan Wu, Omer Akgul +2
Large language models (LLMs) are widely used to fulfill users' information needs; users ask LLMs about the weather, pose educational questions, and consult them for legal assistanc…
Say Something Else: Rethinking Contextual Privacy as Information Sufficiency
Yunze Xiao, Wenkai Li, Xiaoyuan Wu +3
LLM agents increasingly draft messages on behalf of users, yet users routinely overshare sensitive information and disagree on what counts as private. Existing systems support only…
User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios
Xiaoyuan Wu, Roshni Kaushik, Wenkai Li +2
Large language models (LLMs) are rapidly being adopted for tasks like drafting emails, summarizing meetings, and answering health questions. In these settings, users may need to sh…
Estimating LLM Consistency: A User Baseline vs Surrogate Metrics
Xiaoyuan Wu, Weiran Lin, Omer Akgul +1
Large language models (LLMs) are prone to hallucinations and sensitive to prompt perturbations, often resulting in inconsistent or unreliable generated text. Different methods have…
The Impact of Device Type, Data Practices, and Use Case Scenarios on Privacy Concerns about Eye-tracked Augmented Reality in the United States and Germany
Efe Bozkir, Babette Bühler, Xiaoyuan Wu +3
Augmented reality technology will likely be prevalent with more affordable head-mounted displays. Integrating novel interaction modalities, such as eye trackers into head-mounted d…