Privacy Guarantees for Cloud-based State Estimation using Partially Homomorphic Encryption

TL;DR

This paper introduces privacy-preserving protocols for cloud-based state estimation using Kalman filters and partially homomorphic encryption, ensuring data privacy against coalitions while maintaining computational efficiency.

Contribution

It proposes novel cryptographic protocols that enable secure state estimation in cloud environments with formal privacy guarantees.

Findings

Protocols achieve formal indistinguishability privacy guarantees.

Protocols are efficient based on real testbed evaluations.

Effective privacy protection against coalitions in cloud-based systems.

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.