Distributed Differentially Private Control Synthesis for Multi-Agent Systems with Metric Temporal Logic Specifications

TL;DR

This paper introduces a distributed control method for multi-agent systems that ensures privacy and satisfies complex temporal logic specifications using differential privacy, Kalman filtering, and MILP optimization.

Contribution

It presents a novel distributed receding horizon control framework combining differential privacy, Kalman filtering, and MILP for multi-agent systems with temporal logic constraints.

Findings

Successfully maintains agent privacy through noise addition.

Ensures system-level and agent-level specifications are satisfied.

Demonstrates effectiveness via a case study.

Abstract

In this paper, we propose a distributed differentially private receding horizon control (RHC) approach for multi-agent systems (MAS) with metric temporal logic (MTL) specifications. In the MAS considered in this paper, each agent privatizes its sensitive information from other agents using a differential privacy mechanism. In other words, each agent adds privacy noise (e.g., Gaussian noise) to its output to maintain its privacy and communicates its noisy output with its neighboring agents. We define two types of MTL specifications for the MAS: agent-level specifications and system-level specifications. Agents should collaborate to satisfy the system-level MTL specifications with a minimum probability while each agent must satisfy its own agent-level MTL specifications at the same time. In the proposed distributed RHC approach, each agent communicates with its neighboring agents to acquire their noisy outputs and calculates an estimate of the system-level trajectory. Then each agent synthesizes its own control inputs such that the system-level specifications are satisfied with a minimum probability while the agent-level specifications are also satisfied. In the proposed optimization formulation of RHC, we directly incorporate Kalman filter equations to calculate the estimates of the system-level trajectory, and we use mixed-integer linear programming (MILP) to encode the MTL specifications as optimization constraints. Finally, we implement the proposed distributed RHC approach in a case study.