Authors
Quan Zhou, Zhicheng Wang, Zhe Yue, Libin Cai, Kuien Liu, Caiyan Qin
Published in
Sensors (Basel, Switzerland). Volume 26. Issue 17. Aug 26, 2026. Epub Aug 26, 2026.
Abstract
Cloud-based large language model (LLM) services are increasingly used to process natural-language queries that may contain private or sensitive information. Conventional privacy-preserving approaches, such as cryptographic protection and text sanitization, often introduce substantial computational overhead or disrupt the semantic integrity of the original query, resulting in a trade-off between privacy protection and task utility. To address this limitation, we propose generative semantic chaffing (GS-Chaff), a training-free multi-agent framework for privacy-preserving LLM inference over natural-language text queries. Rather than explicitly masking sensitive content, GS-Chaff hides the user's true intent among semantically plausible chaff queries. The framework is implemented through two small language model (SLM)-based agents: a privacy policy agent that adaptively determines the required semantic abstraction level and chaffing factor for each text query, and a generative semantic chaffing agent that produces semantically aligned dummy queries. After cloud-side inference, the response corresponding to the protected real query is recovered locally using a stateless index, without modifying the cloud-based LLM. Experimental results on text-based benchmarks demonstrate that GS-Chaff reduces the attacker's real-query identification rate to 22.5%, close to random guessing, while maintaining inference utility on the evaluated benchmarks. In addition, GS-Chaff reduces local preprocessing time by 1.85× compared with a fixed chaffing configuration using β=5.
PMID:
42740005
Bibliographic data and abstract were imported from PubMed on 15 Sep 2026.
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