Gaussian Mechanism Design for Prescribed Privacy Sets in Data Releasing Systems

Teimour Hosseinalizadeh*, Nima Monshizadeh*

*Corresponding author for this work

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

13 Downloads (Pure)

Abstract

The data transmitted by cyber-physical systems can be intercepted and exploited by malicious individuals to infer privacy-sensitive information regarding the physical system. This motivates us to study the problem of preserving privacy in data releasing of linear dynamical system using stochastic perturbation. In this study, the privacy sensitive quantity is the initial state value of the system where for protecting its privacy, we directly design the covariance matrix of a Gaussian output noise to achieve a prescribed uncertainty set in the form of hyper-ellipsoids. This is done by correlated noise and through a convex optimization problem by considering the utility of released signals. Compared to other available methods, our proposed technique for designing the Gaussian output noise provides enhanced flexibility for system designers. As a case study, the results are applied to a heating ventilation and air conditioning system.

Original languageEnglish
Title of host publication22nd IFAC World Congress
EditorsHideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
PublisherElsevier Bedrijfsinformatie b.v.
Pages1827-1832
Number of pages6
Edition2
ISBN (Electronic)9781713872344
DOIs
Publication statusPublished - 1-Jul-2023
Event22nd IFAC World Congress - Yokohama, Japan
Duration: 9-Jul-202314-Jul-2023

Publication series

NameIFAC-PapersOnLine
Number2
Volume56
ISSN (Electronic)2405-8963

Conference

Conference22nd IFAC World Congress
Country/TerritoryJapan
CityYokohama
Period09/07/202314/07/2023

Keywords

  • Cyber-Physical Systems
  • Gaussian Mechanism
  • Observability Gramian
  • Privacy

Cite this