发表机构
Virginia Tech; ADA University; University of Potsdam; University of Passau（弗吉尼亚理工大学; 阿塞拜疆ADA大学; 波茨坦大学; 帕绍大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究公平性增强算法在亚群体层面如何影响隐私风险，通过调整似然比攻击进行审计，分析差分隐私与公平性方法交互，发现公平性干预对隐私风险影响因多种因素而异，引入统一实证框架支持相关审计。
AI 中文摘要
在医疗、执法和金融等敏感领域部署的机器学习模型不仅要满足效用要求，还必须保证公平性和隐私性。以往研究多关注隐私保护技术对公平性的影响，而公平性增强算法如何影响隐私泄露尚少探索。本文首次全面研究公平性干预如何在亚群体层面影响成员推理隐私风险。通过调整似然比攻击用于亚组审计，发现总体评估掩盖的隐私差异。进一步分析差分隐私与不同类别公平性增强方法的交互，表明差分隐私的隐私收益和效用成本在亚群体中分布不均。结果表明公平性干预并非统一增加隐私风险，其影响取决于模型架构、亚组规模和缓解策略。这些发现揭示公平性、隐私性和效用必须在亚群体层面联合评估，并引入首个统一实证框架支持实践中的此类审计。
英文摘要
Machine learning (ML) models deployed in sensitive domains such as healthcare, law enforcement, and finance must satisfy not only utility requirements but also fairness and privacy guarantees. While prior work has largely examined how privacy-preserving techniques affect fairness, the inverse question-how fairness-enhancing algorithms influence privacy leakage-remains underexplored. We present the first comprehensive study of how fairness interventions affect membership inference privacy risks at the subpopulation level. By adapting the Likelihood Ratio Attack (LiRA) for subgroup auditing, we uncover privacy disparities that aggregate evaluations obscure. We further analyze how Differential Privacy (DP) interacts with fairness-enhancing methods across different categories, showing that DP's privacy benefits and utility costs are unevenly distributed across subpopulations. Our results demonstrate that fairness interventions do not uniformly increase privacy risk; their impact depends on model architecture, subgroup size, and mitigation strategy. These findings reveal that fairness, privacy, and utility must be jointly evaluated at the subpopulation level, and we introduce the first unified empirical framework to support such auditing in practice.
CommentsEuroS&P 2026
