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Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment
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Large Language Models (LLMs) have seen widespread adoption due to their remarkable natural language capabilities. However, when deploying them in real-world settings, it is important to align LLMs to generate texts according to acceptable human standards. Methods such as Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO) have enabled significant progress in refining LLMs using human preference data. However, the privacy concerns inherent in utilizing such preference data have yet to be adequately studied. In this paper, we investigate the vulnerability of LLMs aligned using two widely used methods - DPO and PPO - to membership inference attacks (MIAs). Our study has two main contributions: first, we theoretically motivate that DPO models are more vulnerable to MIA compared to PPO models; second, we introduce a novel reference-based attack framework specifically for analyzing preference data called PREMIA (\uline{Pre}ference data \uline{MIA}). Using PREMIA and existing baselines we empirically show that DPO models have a relatively heightened vulnerability towards MIA.
Forward citations
Cited by 4 Pith papers
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A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO
Under linear-model assumptions, offline RLHF and DPO both reduce to logistic regression, and privatizing labels before corruption (LTC) carries an extra c(ε) factor in the error bounds compared to corrupting before pr...
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Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment
SquareχPO, a square-loss variant of χPO, achieves optimal 1/sqrt(n) suboptimality under label privacy and Huber corruption for offline direct alignment with general function classes.
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Improved Bounds for Private and Robust Alignment
Private MLE log loss is near-optimal for alignment, and square-loss alignment tolerates corruption at the optimal linear rate, including in online exploration.
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ISACL: Internal State Analyzer for Copyrighted Training Data Leakage
An MLP trained on LLM internal states predicts Rouge-L-defined literal copying leakage with high accuracy, but not paraphrase-level leakage.
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