Distributed Kalman filtering techniques enable agents of a multiagent network to enhance their ability to track a system and learn from local cooperation with neighbors. Enabling this cooperation, however, requires agents to share information, which raises the question of privacy. This paper proposes a privacy-preserving distributed Kalman filter (PP-DKF) that protects local information of agents by restricting and obfuscating the information exchanged. The derived PP-DKF embeds two state-of-the-art average consensus techniques that guarantee agent privacy. The resulting PP-DKF utilizes noise injection-based and decomposition-based privacy-preserving techniques to implement a robust distributed Kalman filtering solution against perturbation. We characterize the performance and convergence of the proposed PP-DKF and demonstrate its robustness against the injected noise variance. We also assess the privacy-preserving properties of the proposed algorithm for two types of adversaries, namely, an external eavesdropper and an honest-but-curious (HBC) agent, by providing bounds on the privacy leakage for both adversaries. Finally, several simulation examples illustrate that the proposed PP-DKF achieves better performance and higher privacy levels than the distributed Kalman filtering solutions employing contemporary privacy-preserving techniques.

中文翻译：

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隐私保护分布式卡尔曼滤波

分布式卡尔曼滤波技术使多智能体网络的智能体能够增强其跟踪系统并从与邻居的本地合作中学习的能力。然而，实现这种合作需要代理共享信息，这引发了隐私问题。本文提出了一种隐私保护分布式卡尔曼滤波器（PP-DKF），通过限制和混淆交换的信息来保护代理的本地信息。派生的 PP-DKF 嵌入了两种最先进的平均共识技术，可保证代理隐私。由此产生的 PP-DKF 利用基于噪声注入和基于分解的隐私保护技术来实现鲁棒的分布式卡尔曼滤波解决方案以防止扰动。我们描述了所提出的 PP-DKF 的性能和收敛性，并证明了它对注入噪声方差的鲁棒性。我们还通过为两种攻击者提供隐私泄露界限来评估所提出的算法对两种类型的攻击者的隐私保护特性，即外部窃听者和诚实但好奇 (HBC) 代理。最后，几个仿真示例表明，与采用当代隐私保护技术的分布式卡尔曼滤波解决方案相比，所提出的 PP-DKF 实现了更好的性能和更高的隐私级别。通过为两个对手提供隐私泄露界限。最后，几个仿真示例表明，与采用当代隐私保护技术的分布式卡尔曼滤波解决方案相比，所提出的 PP-DKF 实现了更好的性能和更高的隐私级别。通过为两个对手提供隐私泄露界限。最后，几个仿真示例表明，与采用当代隐私保护技术的分布式卡尔曼滤波解决方案相比，所提出的 PP-DKF 实现了更好的性能和更高的隐私级别。