标题： 从成对比较中获得的最优差分隐私排名 显示英文标题

标题： Optimal Differentially Private Ranking from Pairwise Comparisons

摘要： 数据隐私是许多涉及从不完整和噪声成对比较中进行排序的应用中的核心问题，例如推荐系统、教育评估以及关于敏感话题的意见调查。 在本工作中，我们提出了基于成对比较的差分隐私算法。 具体而言，我们在两种隐私概念下开发并分析了排序方法：边差分隐私，它保护个体比较结果的机密性；个体差分隐私，它保护由单个个体提供的大量比较。 我们的算法——包括一种扰动最大似然估计器和一种基于噪声计数的方法——在各自的隐私约束下被证明达到了最小最大最优收敛率。 我们进一步通过在模拟和真实世界数据上的实验展示了我们方法的实际有效性。 显示英文摘要

摘要： Data privacy is a central concern in many applications involving ranking from incomplete and noisy pairwise comparisons, such as recommendation systems, educational assessments, and opinion surveys on sensitive topics. In this work, we propose differentially private algorithms for ranking based on pairwise comparisons. Specifically, we develop and analyze ranking methods under two privacy notions: edge differential privacy, which protects the confidentiality of individual comparison outcomes, and individual differential privacy, which safeguards potentially many comparisons contributed by a single individual. Our algorithms--including a perturbed maximum likelihood estimator and a noisy count-based method--are shown to achieve minimax optimal rates of convergence under the respective privacy constraints. We further demonstrate the practical effectiveness of our methods through experiments on both simulated and real-world data.