Differentially Private Multi-Agent Planning for Logistic-like Problems

TL;DR

This paper introduces a novel multi-agent planning method for logistic problems that guarantees strong privacy using differential privacy, balancing efficiency, completeness, and communication constraints, and is the first to do so.

Contribution

It is the first to apply differential privacy to multi-agent planning for logistic problems, ensuring privacy while maintaining efficiency and completeness.

Findings

The approach guarantees strong privacy and completeness.

Empirical results show improved efficiency over existing methods.

Theoretical analysis confirms controlled communication overhead.

Abstract

Planning is one of the main approaches used to improve agents' working efficiency by making plans beforehand. However, during planning, agents face the risk of having their private information leaked. This paper proposes a novel strong privacy-preserving planning approach for logistic-like problems. This approach outperforms existing approaches by addressing two challenges: 1) simultaneously achieving strong privacy, completeness and efficiency, and 2) addressing communication constraints. These two challenges are prevalent in many real-world applications including logistics in military environments and packet routing in networks. To tackle these two challenges, our approach adopts the differential privacy technique, which can both guarantee strong privacy and control communication overhead. To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning as a means of preserving the privacy of agents for logistic-like problems. We theoretically prove the strong privacy and completeness of our approach and empirically demonstrate its efficiency. We also theoretically analyze the communication overhead of our approach and illustrate how differential privacy can be used to control it.