Privacy-Preserving State Estimation in the Presence of Eavesdroppers: A Survey
<|reference_start|>Privacy-Preserving State Estimation in the Presence of Eavesdroppers: A Survey: 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.<|reference_end|>
@article{yan2024privacy-preserving,
    title={Privacy-Preserving State Estimation in the Presence of Eavesdroppers: A
  Survey},
    author={Xinhao Yan, Guanzhong Zhou, Daniel E. Quevedo, Carlos Murguia, Bo
  Chen, Hailong Huang},
    journal={arXiv preprint arXiv:2402.15738},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15738},
    primaryClass={cs.CR cs.SY eess.SY}
}