Privacy Preserving Multi Agent Path Finding

2026-05-13

AI Summary

This work addresses the vulnerability of path privacy in multi-agent path finding (MAPF) by formally defining, for the first time, two distinct privacy constraints—planning-level and execution-level—and proposing corresponding solutions. To protect path intentions during planning, a virtual agent mechanism is introduced; to conceal actual movement trajectories during execution, the PIBT and LaCAM algorithms are adapted with privacy-preserving modifications. Additionally, a privacy-aware post-processing optimization technique is developed to further refine solutions. Experimental results demonstrate that the proposed framework significantly reduces total path cost while rigorously preserving both types of privacy, offering a general and efficient approach for MAPF in privacy-sensitive scenarios.

Abstract

In the multi-agent path finding (MAPF) problem, a group of agents search in a graph for a path for each agent where no two paths collide. While in all applications of MAPF the agents must not collide with each other, in some of them the agents may not wish to share their paths due to privacy constraints. In this work, we formulate two types of privacy constraints for MAPF and propose algorithms that preserve them. The first type of privacy we consider is planning-level privacy, which means that during planning, the agents cannot identify exactly the planned location of the other agents. We propose a general framework for obtaining planning-level privacy, which works by adding mock agents to the planning process. The second type of privacy we consider is execution-level privacy, which is relevant when agents have limited sensing capabilities. Execution-level privacy is preserved if none of the agents is allowed to sense the location of the other agents during execution. We show how to adapt two popular MAPF algorithms, namely PIBT and LaCAM, such that they preserve execution-level privacy. Lastly, we propose a post-processing technique that allows the agents to reduce the sum of costs of the returned solution without losing any privacy. We also implemented our algorithms and evaluated them empirically, showing that the proposed post-processing technique indeed improved cost significantly.

Problem

• Multi-Agent Path Finding
• Privacy Preservation
• Planning-Level Privacy
• Execution-Level Privacy

Innovation

• privacy-preserving MAPF
• planning-level privacy
• execution-level privacy
• mock agents
• post-processing optimization