Privacy-Preserving Set-Based Estimation Using Differential Privacy and Zonotopes
For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns arise from disclosing sensitive measurements to a cloud estimator. To solve this issue, we propose a differentially private set-based e...
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Zusammenfassung: | For large-scale cyber-physical systems, the collaboration of spatially
distributed sensors is often needed to perform the state estimation process.
Privacy concerns arise from disclosing sensitive measurements to a cloud
estimator. To solve this issue, we propose a differentially private set-based
estimation protocol that guarantees true state containment in the estimated set
and differential privacy for the sensitive measurements throughout the
set-based state estimation process within the central and local differential
privacy models. Zonotopes are employed in the proposed differentially private
set-based estimator, offering computational advantages in set operations. We
consider a plant of a non-linear discrete-time dynamical system with bounded
modeling uncertainties, sensors that provide sensitive measurements with
bounded measurement uncertainties, and a cloud estimator that predicts the
system's state. The privacy-preserving noise perturbs the centers of
measurement zonotopes, thereby concealing the precise position of these
zonotopes, i.e., ensuring privacy preservation for the sets containing
sensitive measurements. Compared to existing research, our approach achieves
less privacy loss and utility loss through the central and local differential
privacy models by leveraging 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. |
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DOI: | 10.48550/arxiv.2408.17263 |