mlsanityData to 14 Oct 2026

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

arXiv 2505.06122 ↗First posted 9 May 2025eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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.

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.

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8 of 20 references.

References · 8

  1. 2020Discriminative Particle Filter Reinforcement Learning for Complex Partial Observationscited by 28
  2. 2020Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learningcited by 12
  3. 2014Privacy Games: Optimal User-Centric Data Obfuscationcited by 8
  4. 2024Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learningcited by 8
  5. 2020Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stabilitycited by 4
  6. 2024A Parameter Privacy-Preserving Strategy for Mixed-Autonomy Platoon Controlcited by 4
  7. 2021A Model Randomization Approach to Statistical Parameter Privacycited by 3
  8. 2023Optimal Privacy-Aware Dynamic Estimationcited by 1

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  1. 2024Privacy Preserving Reinforcement Learning for Population Processes
  2. 2025Privacy-R1: Privacy-Aware Multi-LLM Agent Collaboration via Reinforcement Learning
  3. 2024Differentially Private Reinforcement Learning with Self-Play
  4. 2025Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs
  5. 2024Preserving Expert-Level Privacy in Offline Reinforcement Learning
  6. 2026Privacy-Preserving Reinforcement Learning from Human Feedback via Decoupled Reward Modeling
  7. 2024Near-Optimal Reinforcement Learning with Shuffle Differential Privacy
  8. 2026Privacy Preserving Reinforcement Learning with One-Sided Feedback