Parameter Privacy-Preserving Data Sharing: A Particle-Belief MDP Formulation
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866912875428184064 |
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| author | Yu, Haokun Zhou, Jingyuan Yang, Kaidi |
| author_facet | Yu, Haokun Zhou, Jingyuan Yang, Kaidi |
| contents | This paper investigates parameter-privacy-preserving data sharing in continuous-state dynamical systems, where a data owner designs a data-sharing policy to support downstream estimation and control while preventing adversarial inference of a sensitive parameter. This data-sharing problem is formulated as an optimization problem that trades off privacy leakage and the impact of data sharing on the data owner's utility, subject to a data-usability constraint. We show that this problem admits an equivalent belief Markov decision process (MDP) formulation, which provides a simplified representation of the optimal policy. To efficiently characterize information-theoretic privacy leakage in continuous state and action spaces, we propose a particle-belief MDP formulation that tracks the parameter posterior via sequential Monte Carlo, yielding a tractable belief-state approximation that converges asymptotically as the number of particles increases. We further derive a tractable closed-form upper bound on particle-based MI via Gaussian mixture approximations, which enables efficient optimization of the particle-belief MDP. Experiments on a mixed-autonomy platoon show that the learned continuous policy substantially impedes inference attacks on human-driving behavior parameters while maintaining data usability and system performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04262 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Parameter Privacy-Preserving Data Sharing: A Particle-Belief MDP Formulation Yu, Haokun Zhou, Jingyuan Yang, Kaidi Systems and Control Optimization and Control This paper investigates parameter-privacy-preserving data sharing in continuous-state dynamical systems, where a data owner designs a data-sharing policy to support downstream estimation and control while preventing adversarial inference of a sensitive parameter. This data-sharing problem is formulated as an optimization problem that trades off privacy leakage and the impact of data sharing on the data owner's utility, subject to a data-usability constraint. We show that this problem admits an equivalent belief Markov decision process (MDP) formulation, which provides a simplified representation of the optimal policy. To efficiently characterize information-theoretic privacy leakage in continuous state and action spaces, we propose a particle-belief MDP formulation that tracks the parameter posterior via sequential Monte Carlo, yielding a tractable belief-state approximation that converges asymptotically as the number of particles increases. We further derive a tractable closed-form upper bound on particle-based MI via Gaussian mixture approximations, which enables efficient optimization of the particle-belief MDP. Experiments on a mixed-autonomy platoon show that the learned continuous policy substantially impedes inference attacks on human-driving behavior parameters while maintaining data usability and system performance. |
| title | Parameter Privacy-Preserving Data Sharing: A Particle-Belief MDP Formulation |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2602.04262 |