Privacy against state estimation: An optimization framework based on the data processing inequality

Carlos Murguia, Iman Shames, Farhad Farokhi, Dragan Nešic

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Abstract

Information about the system state is obtained through noisy sensor measurements. This data is coded and transmitted to a trusted user through an unsecured communication network. We aim at keeping the system state private; however, because the network is not secure, opponents might access sensor data, which can be used to estimate the state. To prevent this, before transmission, we randomize coded sensor data by passing it through a probabilistic mapping, and send the corrupted data to the trusted user. Making use of the data processing inequality, we cast the synthesis of the probabilistic mapping as a convex program where we minimize the mutual information (our privacy metric) between two estimators, one constructed using the randomized sensor data and the other using the actual undistorted sensor measurements, for a desired level of distortion-how different coded sensor measurements and distorted data are allowed to be.

Original languageEnglish
Pages (from-to)7368-7373
Number of pages6
JournalIFAC-PapersOnLine
Volume53
Issue number2
DOIs
Publication statusPublished - 2020
Event21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress) - Berlin, Germany
Duration: 12 Jul 202017 Jul 2020
Conference number: 21
https://www.ifac2020.org/

Bibliographical note

Funding Information:
This work was supported by the Australian Research Council (ARC) under the Project DP170104099; and the NATO Science for Peace and Security (SPS) PROGRAMME under the project SPS.SFP G5479.

Keywords

  • Data processing inequality
  • Mutual information
  • Privacy
  • Stochastic systems

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