近似稳定、学校最优和学生真实的多对一匹配（通过差分隐私）
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy)
摘要：
我们提出了一种计算渐近稳定的学校最优匹配的机制，同时保证它是一个渐进的优势策略，每个学生报告他们的真实偏好的机制。我们在这方面的努力的主要工具是差分隐私：我们给出了一个算法，协调一个稳定的匹配使用差异私人信号，这导致我们的真实性保证。这是第一个设置，它是已知的如何实现非平凡的真实性保证的学生时，计算学校的最佳匹配，假设最坏情况下的偏好（学校和学生）在大市场。
英文摘要：
We present a mechanism for computing asymptotically stable school optimal matchings, while guaranteeing that it is an asymptotic dominant strategy for every student to report their true preferences to the mechanism. Our main tool in this endeavor is differential privacy: we give an algorithm that coordinates a stable matching using differentially private signals, which lead to our truthfulness guarantee. This is the first setting in which it is known how to achieve nontrivial truthfulness guarantees for students when computing school optimal matchings, assuming worst- case preferences (for schools and students) in large markets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
