Dual-Protection Method Against Eavesdroppers for Distributed State Estimation

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-04-23 DOI:10.1109/TSIPN.2025.3563774

Yan Yu;Chao Yang;Wenjie Ding;Wen Yang;Xiaofan Wang

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引用次数: 0

Abstract

This paper examines a security issue for state estimation over a wireless sensor network. The state estimates are transmitted among neighboring nodes through wireless channels in a distributed network, wherein the transmission of the data are vulnerable to the intercept from eavesdroppers, leading to important data privacy leakage. To prevent eavesdroppers from obtaining state estimates, we propose a dual-protection method that combines dynamic transformation with lightweight encryption, which aims to protect the privacy without raising suspicion from eavesdroppers. Furthermore, we consider the scenarios where eavesdroppers utilize side-channel information to gather data and attempt to deduce the encryption mechanism, subsequently inferring the real state estimate. We also provide the analysis to show that the eavesdropper with inference capabilities could not influence the estimation performance of sensors. Finally, the numerical examples are provided to illustrate the effectiveness of the privacy-preserving method.

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分布式状态估计防窃听双保护方法

本文研究了无线传感器网络状态估计的安全问题。在分布式网络中，状态估计通过无线信道在相邻节点之间传输，数据的传输容易被窃听者拦截，导致重要的数据隐私泄露。为了防止被窃听者获取状态估计，我们提出了一种将动态转换与轻量级加密相结合的双重保护方法，既保护隐私又不引起窃听者的怀疑。此外，我们还考虑了窃听者利用侧信道信息收集数据并试图推断加密机制的场景，从而推断出真实状态估计。分析表明，具有推理能力的窃听者不会影响传感器的估计性能。最后，通过数值算例说明了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.