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  3. Privacy-preserving model predictive control using secure multi-party computation

Privacy-preserving model predictive control using secure multi-party computation

Adelipour, S ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.1109/ICEE59167.2023.10334878
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2023
  4. Abstract:
  5. In this paper, a secure multi-party computation strategy based on secret sharing is used to derive a privacy-preserving model predictive control for a class of cyber-physical systems. In the proposed framework, the underlying optimization problem of model predictive control is solved by a variation of projected gradient method. All required computations are carried out by outsourced computation units at the cloud level, while data privacy is maintained using a secret sharing scheme. The original values of system private parameters are not revealed to any external eavesdroppers and the cloud computing units. Simulation results demonstrate the efficiency of the proposed method in terms of performance and privacy. © 2023 IEEE
  6. Keywords:
  7. Model predictive control ; Multi-party computation ; Privacy-preserving optimization ; Projected gradient method ; Secret sharing
  8. Source: 2023 31st International Conference on Electrical Engineering, ICEE 2023 ; 2023 , Pages 915-919 ; 979-835031256-0 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/10334878