差分隐私卡尔曼滤波
Differentially Private Kalman Filtering
AI总结：
针对需要保护个体数据隐私的H2（卡尔曼）滤波问题，提出基于差分隐私的机制，并通过控制H∞范数减轻隐私机制对估计性能的影响。
摘要：
本文研究了在必须基于个体参与者输入构建信号估计且这些数据必须保持私密的情况下的H2（卡尔曼）滤波问题。该问题出现在新兴应用中，如智能电网或智能交通系统，其中用户不断向执行全局监测或控制任务的第三方聚合器发送数据，并要求保证这些数据不能用于推断额外的个人信息。为了针对具有任意侧信息的对手提供强大的正式隐私保证，我们依赖于相对较新引入数据库文献中的差分隐私概念。将此概念扩展到具有许多贡献独立输入信号的参与者的动态系统，然后提出机制以解决具有差分隐私约束的H2滤波问题。描述了一种减轻隐私诱导机制对估计性能影响的方法，该方法依赖于控制滤波器的H∞范数。最后，我们讨论了在隐私保护交通监控系统中的应用。
英文摘要：
This paper studies the H2 (Kalman) filtering problem in the situation where a signal estimate must be constructed based on inputs from individual participants, whose data must remain private. This problem arises in emerging applications such as smart grids or intelligent transportation systems, where users continuously send data to third-party aggregators performing global monitoring or control tasks, and require guarantees that this data cannot be used to infer additional personal information. To provide strong formal privacy guarantees against adversaries with arbitrary side information, we rely on the notion of differential privacy introduced relatively recently in the database literature. This notion is extended to dynamic systems with many participants contributing independent input signals, and mechanisms are then proposed to solve the H2 filtering problem with a differential privacy constraint. A method for mitigating the impact of the privacy-inducing mechanism on the estimation performance is described, which relies on controlling the Hinfinity norm of the filter. Finally, we discuss an application to a privacy-preserving traffic monitoring system.
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。
