Differentially Private Controller Synthesis With Metric Temporal Logic Specifications

TL;DR

This paper introduces a method for synthesizing controllers for multi-agent systems that ensures differential privacy and high-level metric temporal logic specifications, using noisy data sharing and probabilistic guarantees.

Contribution

It presents a novel approach combining differential privacy with MTL-based control synthesis in multi-agent systems with a cloud-based control architecture.

Findings

Successfully applied to a simulation with two Baxter-On-Wheels robots.

Achieved differential privacy while satisfying MTL specifications probabilistically.

Demonstrated effectiveness of privacy-preserving control in multi-agent scenarios.

Abstract

Privacy is an important concern in various multiagent systems in which data collected from the agents are sensitive. We propose a differentially private controller synthesis approach for multi-agent systems subject to high-level specifications expressed in metric temporal logic (MTL). We consider a setting where each agent sends data to a cloud (computing station) through a set of local hubs and the cloud is responsible for computing the control inputs of the agents. Specifically, each agent adds privacy noise (e.g., Gaussian noise) point-wise in time to its own outputs before sharing them with a local hub. Each local hub runs a Kalman filter to estimate the state of the corresponding agent and periodically sends such state estimates to the cloud. The cloud computes the optimal inputs for each agent subject to an MTL specification. While guaranteeing differential privacy of each agent, the controller is also synthesized to ensure a probabilistic guarantee for satisfying the MTL specification.We provide an implementation of the proposed method on a simulation case study with two Baxter-On-Wheels robots as the agents.