Rescriber: Smaller-LLM-Powered User-Led Data Minimization for LLM-Based Chatbots

要旨

The proliferation of LLM-based conversational agents has resulted in excessive disclosure of identifiable or sensitive information. However, existing technologies fail to offer perceptible control or account for users’ personal preferences about privacy-utility tradeoffs due to the lack of user involvement. To bridge this gap, we designed, built, and evaluated Rescriber, a browser extension that supports user-led data minimization in LLM-based conversational agents by helping users detect and sanitize personal information in their prompts. Our studies (N=12) showed that Rescriber helped users reduce unnecessary disclosure and addressed their privacy concerns. Users’ subjective perceptions of the system powered by Llama3-8B were on par with that by GPT-4o. The comprehensiveness and consistency of the detection and sanitization emerge as essential factors that affect users’ trust and perceived protection. Our findings confirm the viability of smaller-LLM-powered, user-facing, on-device privacy controls, presenting a promising approach to address the privacy and trust challenges of AI.

著者
Jijie Zhou
Northeastern University, Boston, Massachusetts, United States
Eryue Xu
Northeastern University, Boston, Massachusetts, United States
Yaoyao Wu
Northeastern University, Boston, Massachusetts, United States
Tianshi Li
Northeastern University, Boston, Massachusetts, United States
DOI

10.1145/3706598.3713701

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713701

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: DeIving into LLMs

G303
7 件の発表
2025-04-29 20:10:00
2025-04-29 21:40:00
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