  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.
