Privacy Guarantees for Cloud-based State Estimation using Partially Homomorphic EncryptionShow others and affiliations
2022 (English)In: 2022 European Control Conference (ECC), IEEE , 2022, p. 98-105Conference paper, Published paper (Refereed)
Abstract [en]
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.
Place, publisher, year, edition, pages
IEEE , 2022. p. 98-105
Keywords [en]
Kalman filter, estimation, computational privacy
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-320660DOI: 10.23919/ECC55457.2022.9838094ISI: 000857432300012Scopus ID: 2-s2.0-85136738507OAI: oai:DiVA.org:kth-320660DiVA, id: diva2:1707606
Conference
European Control Conference (ECC), JUL 12-15, 2022, London, ENGLAND
Note
Part of proceedings: ISBN 978-3-907144-07-7, QC 20221101
2022-11-012022-11-012023-05-15Bibliographically approved