论文标题
一种分布式的私人算法，用于在不可约束的设置中用于资源分配
A Distributed Differentially Private Algorithm for Resource Allocation in Unboundedly Large Settings
论文作者
论文摘要
我们引入了一种实用且可扩展的算法（PALMA），用于解决多代理系统的基本问题之一 - 查找匹配和分配 - 在无限制的设置中（例如，在城市环境中的资源分配，启用式机动性的系统等），同时提供强大的最差隐私保证。帕尔玛（Palma）是分散的，在设备上运行，不需要经常沟通，并且在合理的假设下会在恒定时间内收敛。我们使用两者中的真实数据来评估Palma的行动能力和纸质作业场景，并证明它提供了强大的隐私水平（$ \ varepsilon \ lepsilon \ leq 1 $，中位数低至$ \ varepsilon = 0.5 $ $ \ varepsilon = 0.5 $）和高质量匹配（最高范围$ $ $ $ $ $ $ $ $ $ $ $ $ $ $普遍企业，最大重量匹配基线）。
We introduce a practical and scalable algorithm (PALMA) for solving one of the fundamental problems of multi-agent systems -- finding matches and allocations -- in unboundedly large settings (e.g., resource allocation in urban environments, mobility-on-demand systems, etc.), while providing strong worst-case privacy guarantees. PALMA is decentralized, runs on-device, requires no inter-agent communication, and converges in constant time under reasonable assumptions. We evaluate PALMA in a mobility-on-demand and a paper assignment scenario, using real data in both, and demonstrate that it provides a strong level of privacy ($\varepsilon \leq 1$ and median as low as $\varepsilon = 0.5$ across agents) and high-quality matchings (up to $86\%$ of the non-private optimal, outperforming even the privacy-preserving centralized maximum-weight matching baseline).
