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

Sawsan Emad, Amr Alanwar, Yousra Alkabani, M. Watheq El-Kharashi, Henrik Sandberg, Karl Henrik Johansson

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

4 Zitate (Scopus)

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.

OriginalspracheEnglisch
Titel2022 European Control Conference, ECC 2022
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten98-105
Seitenumfang8
ISBN (elektronisch)9783907144077
DOIs
PublikationsstatusVeröffentlicht - 2022
Extern publiziertJa
Veranstaltung2022 European Control Conference, ECC 2022 - London, Großbritannien/Vereinigtes Königreich
Dauer: 12 Juli 202215 Juli 2022

Publikationsreihe

Name2022 European Control Conference, ECC 2022

Konferenz

Konferenz2022 European Control Conference, ECC 2022
Land/GebietGroßbritannien/Vereinigtes Königreich
OrtLondon
Zeitraum12/07/2215/07/22

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