Differentially Private Set-Based Estimation Using Zonotopes

Mohammed M. Dawoud, Changxin Liu, Amr Alanwar, Karl H. Johansson

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


For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns naturally arise from disclosing sensitive measurement signals to a cloud estimator that predicts the system state. To solve this issue, we propose a differentially private set-based estimation protocol that preserves the privacy of the measurement signals. Compared to existing research, our approach achieves less privacy loss and utility loss using a numerically optimized truncated noise distribution. The proposed estimator is perturbed by weaker noise than the analytical approaches in the literature to guarantee the same level of privacy, therefore improving the estimation utility. Numerical and comparison experiments with truncated Laplace noise are presented to support our approach. Zonotopes, a less conservative form of set representation, are used to represent estimation sets, giving set operations a computational advantage. The privacy-preserving noise anonymizes the centers of these estimated zonotopes, concealing the precise positions of the estimated zonotopes.

Original languageEnglish
Title of host publication2023 European Control Conference, ECC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9783907144084
StatePublished - 2023
Externally publishedYes
Event2023 European Control Conference, ECC 2023 - Bucharest, Romania
Duration: 13 Jun 202316 Jun 2023

Publication series

Name2023 European Control Conference, ECC 2023


Conference2023 European Control Conference, ECC 2023


  • differential privacy
  • set-based estimation
  • truncated noise distribution
  • zonotopes


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