Privacy Guarantees for Cloud-based State Estimation using Partially Homomorphic Encryption
Abstract
The privacy aspect of state estimation algorithms has been drawing high research attention due to the necessity for a trustworthy private environment in cyber-physical systems. These systems usually engage cloud-computing platforms to aggregate essential information from spatially distributed nodes and produce desired estimates. The exchange of sensitive data among semi-honest parties raises privacy concerns, especially when there are coalitions between parties. We propose two privacy-preserving protocols using Kalman filter and partially homomorphic encryption of the measurements and estimates while exposing the covariances and other model parameters. We prove that the proposed protocols achieve satisfying computational privacy guarantees against various coalitions based on formal cryptographic definitions of indistinguishability. We evaluate the proposed protocols to demonstrate their efficiency using data from a real testbed.
Cite
@article{arxiv.2111.04818,
title = {Privacy Guarantees for Cloud-based State Estimation using Partially Homomorphic Encryption},
author = {Sawsan Emad and Amr Alanwar and Yousra Alkabani and M. Watheq El-Kharashi and Henrik Sandberg and Karl H. Johansson},
journal= {arXiv preprint arXiv:2111.04818},
year = {2022}
}
Comments
Accepted at the 20th European Control Conference (ECC 2022)