Interaction-Aware Parameter Privacy-Preserving Data Sharing in Coupled Systems via Particle Filter Reinforcement Learning

TL;DR

This paper introduces an interaction-aware, privacy-preserving data sharing method using particle filter reinforcement learning to protect sensitive parameters in coupled systems while maintaining system performance.

Contribution

It proposes a novel privacy-preserving approach that minimizes information leakage and control impact, formulated as a Bellman equation and solved with an efficient particle filter RL method.

Findings

Effectively protects sensitive driving parameters against inference attacks.

Maintains negligible impact on fuel efficiency in a platoon scenario.

Reduces history dependency compared to existing RL methods.

Abstract

This paper addresses the problem of parameter privacy-preserving data sharing in coupled systems, where a data provider shares data with a data user but wants to protect its sensitive parameters. The shared data affects not only the data user's decision-making but also the data provider's operations through system interactions. To trade off control performance and privacy, we propose an interaction-aware privacy-preserving data sharing approach. Our approach generates distorted data by minimizing a combination of (i) mutual information, quantifying privacy leakage of sensitive parameters, and (ii) the impact of distorted data on the data provider's control performance, considering the interactions between stakeholders. The optimization problem is formulated into a Bellman equation and solved by a particle filter reinforcement learning (RL)-based approach. Compared to existing RL-based methods, our formulation significantly reduces history dependency and efficiently handles scenarios with continuous state space. Validated in a mixed-autonomy platoon scenario, our method effectively protects sensitive driving behavior parameters of human-driven vehicles (HDVs) against inference attacks while maintaining negligible impact on fuel efficiency.