Privacy Guarantees for Cloud-based State Estimation using Partially Homomorphic Encryption
This addresses privacy concerns for cyber-physical systems using cloud computing, but it is incremental as it builds on existing encryption and estimation methods.
The paper tackled privacy in cloud-based state estimation for cyber-physical systems by proposing two protocols using Kalman filter and partially homomorphic encryption, achieving computational privacy guarantees against coalitions and demonstrating efficiency with real testbed data.
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.