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

arXiv: https://arxiv.org/abs/2505.06122 · HTML: https://mlsanity.com/paper/2505.06122/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 9 May 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 8 of 8)

- [Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations](https://mlsanity.com/paper/2002.09884/) (2020)
- [Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.02685/) (2020)
- [Privacy Games: Optimal User-Centric Data Obfuscation](https://mlsanity.com/paper/1402.3426/) (2014)
- [Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning](https://mlsanity.com/paper/2401.11148/) (2024)
- [Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stability](https://mlsanity.com/paper/2012.04313/) (2020)
- [A Parameter Privacy-Preserving Strategy for Mixed-Autonomy Platoon Control](https://mlsanity.com/paper/2401.15561/) (2024)
- [A Model Randomization Approach to Statistical Parameter Privacy](https://mlsanity.com/paper/2105.10664/) (2021)
- [Optimal Privacy-Aware Dynamic Estimation](https://mlsanity.com/paper/2311.05896/) (2023)

## Abstract (arXiv)

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.