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The paper introduces TRACE-RPS, a novel defense framework against attribute inference attacks in LLMs, which combines fine-grained anonymization with inference-preventing optimization. TRACE uses attention mechanisms and inference chain generation to pinpoint and anonymize privacy-leaking text, while RPS employs a two-stage optimization to encourage models to reject attribute inference queries. Experiments demonstrate that TRACE-RPS significantly reduces attribute inference accuracy on open-source LLMs, achieving strong cross-model generalization, prompt-variation robustness, and utility-privacy tradeoffs.
LLMs can't infer your age or location if you use TRACE-RPS, a new defense that drops attribute inference accuracy from 50% to below 5%.
Recent studies have shown that large language models (LLMs) can infer private user attributes (e.g., age, location, gender) from user-generated text shared online, enabling rapid and large-scale privacy breaches. Existing anonymization-based defenses are coarse-grained, lacking word-level precision in anonymizing privacy-leaking elements. Moreover, they are inherently limited as altering user text to hide sensitive cues still allows attribute inference to occur through models'reasoning capabilities. To address these limitations, we propose a unified defense framework that combines fine-grained anonymization (TRACE) with inference-preventing optimization (RPS). TRACE leverages attention mechanisms and inference chain generation to identify and anonymize privacy-leaking textual elements, while RPS employs a lightweight two-stage optimization strategy to induce model rejection behaviors, thereby preventing attribute inference. Evaluations across diverse LLMs show that TRACE-RPS reduces attribute inference accuracy from around 50\% to below 5\% on open-source models. In addition, our approach offers strong cross-model generalization, prompt-variation robustness, and utility-privacy tradeoffs. Our code is available at https://github.com/Jasper-Yan/TRACE-RPS.