标题： 多传感器系统在多重数据包丢失情况下的隐私保护融合 显示英文标题

标题： Privacy-Preserving Fusion for Multi-Sensor Systems Under Multiple Packet Dropouts

摘要： 无线传感器网络（WSNs）是现代信息物理系统中的关键组成部分，通过空间分布的传感器实现高效的数据采集和融合。 然而，此类网络中固有的窃听和数据包丢失风险对安全状态估计构成了重大挑战。 在本文中，我们研究了在多个数据包丢失和窃听攻击下多传感器系统的隐私保护融合估计（PPFE）问题。 为了缓解这些问题，我们在控制理论框架内提出了一种基于分布式编码的隐私保护机制（PPM），在传输过程中确保数据隐私的同时保持合法状态估计的性能。 开发了一种集中式融合滤波器，考虑了数据包丢失和基于编码的PPM的耦合效应。 通过修改的代数Riccati方程推导了合法用户估计误差协方差的有界性条件。 此外，通过证明窃听者均值估计误差的发散性，严格分析了所提出的PPFE算法的数据保密性。 针对基于互联网的三水箱系统的仿真结果验证了所提方法的有效性，突显了其在不牺牲估计精度的情况下增强隐私的潜力。 显示英文摘要

摘要： Wireless sensor networks (WSNs) are critical components in modern cyber-physical systems, enabling efficient data collection and fusion through spatially distributed sensors. However, the inherent risks of eavesdropping and packet dropouts in such networks pose significant challenges to secure state estimation. In this paper, we address the privacy-preserving fusion estimation (PPFE) problem for multi-sensor systems under multiple packet dropouts and eavesdropping attacks. To mitigate these issues, we propose a distributed encoding-based privacy-preserving mechanism (PPM) within a control-theoretic framework, ensuring data privacy during transmission while maintaining the performance of legitimate state estimation. A centralized fusion filter is developed, accounting for the coupling effects of packet dropouts and the encoding-based PPM. Boundedness conditions for the legitimate user's estimation error covariance are derived via a modified algebraic Riccati equation. Additionally, by demonstrating the divergence of the eavesdropper's mean estimation error, the proposed PPFE algorithm's data confidentiality is rigorously analyzed. Simulation results for an Internet-based three-tank system validate the effectiveness of the proposed approach, highlighting its potential to enhance privacy without compromising estimation accuracy.