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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


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.

Original languageEnglish
Title of host publication2022 European Control Conference, ECC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9783907144077
StatePublished - 2022
Externally publishedYes
Event2022 European Control Conference, ECC 2022 - London, United Kingdom
Duration: 12 Jul 202215 Jul 2022

Publication series

Name2022 European Control Conference, ECC 2022


Conference2022 European Control Conference, ECC 2022
Country/TerritoryUnited Kingdom


  • Kalman filter
  • computational privacy
  • estimation


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