AI 中文总结
研究稳定婚姻算法的隐私攻击问题，假设攻击者可与算法反复交互，揭示非恶意方隐私偏好，发现恶意提议方的Gale-Shapley算法易受攻击，特定偏好分布下诚实方提议的该算法可保护隐私，实验表明真实数据易受攻击，凸显新隐私保护算法的必要性。
AI 中文摘要
稳定婚姻问题出现在许多隐私敏感领域，如美国国家住院医师匹配计划。在这类应用中，保护用户偏好列表的隐私对于防止策略操纵、抑制误报和遵守数据保护法规至关重要。本文研究对稳定婚姻算法的隐私攻击。假设攻击者（如医院）能与稳定婚姻算法反复交互，演示了这种交互如何揭示非恶意方（如住院医师）的隐私偏好。表明广泛应用的Gale-Shapley匹配算法在提议方恶意时易受隐私攻击，所有诚实代理的偏好都可能被揭示。还研究了诚实非恶意方的哪些偏好分布易受攻击，以及诚实方提议时的Gale-Shapley匹配算法在不易受攻击的偏好分布中可保护隐私。将结果扩展到分散设置，表明攻击者能推断所有偏好排序。通过实验评估，测试了对合成数据和真实数据的隐私攻击，显示真实数据确实易受攻击。这项工作强调了对新的隐私保护稳定婚姻算法的需求。
英文摘要
The stable marriage problem appears in many privacy-sensitive domains, for example in the National Resident Matching Program in the US. In such applications, preserving the privacy of users' preference lists is essential to prevent strategic manipulation, discourage misreporting, and comply with data protection regulations. In this work, we investigate privacy attacks on stable marriage algorithms. Assuming that the attacker (e.g., the hospitals) can repeatedly interact with the stable marriage algorithm, we demonstrate how such interactions can reveal private preferences of the non-malicious side (e.g., the residents). We show that the widely applied Gale-Shapley Matching Algorithm, where the proposers' side is malicious, is vulnerable to privacy attacks and all honest agents' preferences can be revealed. We further investigate which preference distributions of the honest, non-malicious side are susceptible to privacy attacks and show that the Gale-Shapley Matching Algorithm where the honest side proposes can preserve privacy in non-susceptible preference distributions. We extend our results to the decentralized setting and show that the attacker's side can infer all preference orderings. In an experimental evaluation, we test privacy attacks on synthetic and real-world data and show that real-world data is indeed susceptible to privacy attacks. This work underlines a need for new privacy-preserving stable marriage algorithms.
CommentsAccepted and presented at the 2026 IEEE International Conference on Distributed Computing Systems (ICDCS 2026)
