3 papers
cs.CR2026
Understanding Help Seeking for Digital Privacy, Safety, and Security
Kurt Thomas, Sai Teja Peddinti, Sarah Meiklejohn +7
The complexity of navigating digital privacy, safety, and security threats often falls directly on users. This leads to users seeking help from family and peers, platforms and advi…
cs.CR2026
LLM-Powered Analysis of IoT User Reviews: Tracking and Ranking Security and Privacy Concerns
Taufiq Islam Protick, Sai Teja Peddinti, Nina Taft +1
Being able to understand the security and privacy (S&P) concerns of IoT users brings benefits to both developers and users. To learn about users' views, we examine Amazon IoT revie…
cs.HC2025
Beyond PII: How Users Attempt to Estimate and Mitigate Implicit LLM Inference
Synthia Wang, Sai Teja Peddinti, Nina Taft +1
Large Language Models (LLMs) such as ChatGPT can infer personal attributes from seemingly innocuous text, raising privacy risks beyond memorized data leakage. While prior work has…