A Distributed Differentially Private Algorithm for Resource Allocation in Unboundedly Large Settings

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 (𝜀 ≤ 1 and median as low as 𝜀 = 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).