8 citations · 28 across the 14 of their papers we have counts for
4 papers · 1 filter
HelpBench: Assessing the Ability of LLMs to Provide Privacy, Safety, and Security Advice
Sarah Meiklejohn, Sunny Consolvo, Patrick Gage Kelley +5
This paper introduces HelpBench, a benchmark for assessing whether LLMs are capable of providing accurate help in response to questions about digital privacy, safety, and security.…
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…
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…
LLMs for Cyber Security: New Opportunities
Dinil Mon Divakaran, Sai Teja Peddinti
Large language models (LLMs) are a class of powerful and versatile models that are beneficial to many industries. With the emergence of LLMs, we take a fresh look at cyber security…