9 papers
What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale
Rameen Mahmood, Tousif Ahmed, Sai Teja Peddinti +1
The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountability. This challenge is especially…
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 U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI
Jiaxun Cao, Yu Dong, Chunxi Zhan +3
Users increasingly rely on consumer-facing generative AI (GenAI) for tasks ranging from everyday needs to sensitive use cases. Yet, it remains unclear whether and how existing secu…
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…
Automated Generation of Accurate Privacy Captions From Android Source Code Using Large Language Models
Vijayanta Jain, Sepideh Ghanavati, Sai Teja Peddinti +1
Privacy captions are short sentences that succinctly describe what personal information is used, how it is used, and why, within an app. These captions can be utilized in various n…
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…