参数隐私保护的数据共享：粒子信念MDP公式
Parameter Privacy-Preserving Data Sharing: A Particle-Belief MDP Formulation
AI总结：
本文提出粒子信念MDP方法，用于连续状态动态系统中保护参数隐私的数据共享问题，通过高效优化策略有效防止敏感参数被推断。
摘要：
本文研究了连续状态动态系统中的参数隐私保护数据共享问题，其中数据拥有者设计数据共享策略以支持后续的估计和控制，同时防止对抗性推断敏感参数。该数据共享问题被 formulations 为一个优化问题，该问题在数据可用性约束下权衡隐私泄露和数据共享对数据拥有者效用的影响。我们证明该问题等价于信念马尔可夫决策过程（MDP）的公式，这为最优策略提供了简化表示。为了高效表征连续状态和动作空间中的信息论隐私泄露，我们提出了一种粒子信念MDP公式，通过序列蒙特卡洛跟踪参数后验，得到一个可处理的信念状态近似，随着粒子数的增加而渐近收敛。我们进一步推导了基于粒子的MI的可处理闭式上界，通过高斯混合近似，这使得粒子信念MDP的优化变得高效。在混合自主车群的实验中，学习的连续策略显著阻碍了对人类驾驶行为参数的推断攻击，同时保持数据可用性和系统性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
