Networked systems are increasingly the target of cyberattacks that exploit vulnerabilities within digital communications, embedded hardware, and software. Arguably, the simplest class of attacks -- and often the first type before launching destructive integrity attacks -- are eavesdropping attacks, which aim to infer information by collecting system data and exploiting it for malicious purposes. A key technology of networked systems is state estimation, which leverages sensing and actuation data and first-principles models to enable trajectory planning, real-time monitoring, and control. However, state estimation can also be exploited by eavesdroppers to identify models and reconstruct states with the aim of, e.g., launching integrity (stealthy) attacks and inferring sensitive information. It is therefore crucial to protect disclosed system data to avoid an accurate state estimation by eavesdroppers. This survey presents a comprehensive review of existing literature on privacy-preserving state estimation methods, while also identifying potential limitations and research gaps. Our primary focus revolves around three types of methods: cryptography, data perturbation, and transmission scheduling, with particular emphasis on Kalman-like filters. Within these categories, we delve into the concepts of homomorphic encryption and differential privacy, which have been extensively investigated in recent years in the context of privacy-preserving state estimation. Finally, we shed light on several technical and fundamental challenges surrounding current methods and propose potential directions for future research.

翻译：网络化系统日益成为利用数字通信、嵌入式硬件和软件漏洞的网络攻击目标。可以说，最简单的一类攻击——通常也是发动破坏性完整性攻击前的第一步——是窃听攻击，其目的是通过收集系统数据并利用其进行恶意用途来推断信息。状态估计是网络化系统的关键技术，它利用传感与驱动数据以及基于第一性原理的模型，实现轨迹规划、实时监测和控制。然而，窃听者也可能利用状态估计来识别模型并重构状态，例如发动完整性（隐蔽式）攻击或推断敏感信息。因此，保护公开的系统数据以防止窃听者进行精确的状态估计至关重要。本综述全面回顾了现有关于隐私保护状态估计方法的文献，同时指出了潜在的限制和研究空白。我们主要关注三类方法：密码学、数据扰动和传输调度，特别重点研究卡尔曼类滤波器。在这些类别中，我们深入探讨了同态加密和差分隐私这些概念，它们近年来在隐私保护状态估计背景下得到了广泛研究。最后，我们揭示了现有方法面临的若干技术和基础性挑战，并提出了未来研究可能的方向。