Danassis, PanayiotisTriastcyn, AlekseiFaltings, Boi2023-01-262023-01-262023-01-262022-05-0910.5555/3535850.3535888https://infoscience.epfl.ch/handle/20.500.14299/194319We 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 ($\varepsilon łeq 1$ 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).text::conference output::conference proceedings::conference paper