Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment

• URL: http://arxiv.org/abs/2407.06443v2
• Date: Sun, 27 Apr 2025 21:07:19 GMT
• Title: Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment
• Authors: Qizhang Feng, Siva Rajesh Kasa, Santhosh Kumar Kasa, Hyokun Yun, Choon Hui Teo, Sravan Babu Bodapati,
• Abstract summary: We investigate the vulnerability of Large Language Models aligned using two methods - DPO and PPO - to membership inference attacks (MIAs)<n>We introduce a novel reference-based attack framework specifically for analyzing preference data called PREMIA (ulinePreference data ulineMIA)
• Score: 7.577032515701217
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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