Privacy of Dynamical Systems

Differential privacy is a natural candidate to alleviate privacy concerns in general. However, differential privacy literature most often deals with providing privacy-preserving responses to queries based on large, yet static datasets that are kept securely by a data curator while, in real-time analytics, the underlying data in possession of the curator changes over time. The composition rule of differential privacy implies that the magnitude of the additive noise that ensures differential privacy must grow rapidly, or that the privacy budget of each response must decrease correspondingly, to ensure that the entire privacy budget remains bounded. In information-theoretic privacy, so far, nothing is known about dynamic scenarios as well, i.e., when the underlying dataset evolves overtime and responses to queries must be provided continually. Multi-party secure computation and homomorphic encryption techniques are also rarely used in real-time control when fast decisions are required. Therefore, there is a need to investigate privacy of dynamical systems.

  • F. Farokhi, “Privacy of Dynamical Systems,” Springer Nature, Singapore, 2020.
  • F. Farokhi, “Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon,” in Proceedings of the 11th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), Apr 22-24, 2020, Sydney, Australia.
  • C. Murguia, I. Shames, F. Farokhi, D. Nesic, “Privacy Against State Estimation: An Optimization Framework based on the Data Processing Inequality,” Submitted.
  • Y. Lin, F. Farokhi, I. Shames, D. Nešić, “Secure Networked Control Systems Design Using Semi- Homomorphic Encryption,” in R. Ferrari, A. Teixeira, (Eds.), Safety, Security, and Privacy for Cyber-Physical Systems, Springer, 2020.
  • C. Murguia, I. Shames, F. Farokhi, and D Nešić, “Information-Theoretic Privacy through Chaos Syn- chronization and Optimal Additive Noise,” in F. Farokhi, (Eds.), Privacy of Dynamical Systems, Internet of Things, Springer, 2020.
  • J. Tran, F. Farokhi, M. Cantoni, I. Shames, “Implementing Homomorphic Encryption Based Secure Feedback Control,” Control Engineering Practice, 2020.
  • F. Farokhi, “Differential Privacy for Evolving Almost-Periodic Datasets with Continual Linear Queries: Application to Energy Data Privacy,” Submitted.
  • C. Murguia, I. Shames, F. Farokhi, D. Nesic, H. V. Poor, “On Privacy of Dynamical Systems: An Optimal Probabilistic Mapping Approach,” Submitted.
  • M. Schulze Darup, A. Redder, I. Shames, F. Farokhi, D. E. Quevedo, “Towards Encrypted MPC for Linear Constrained Systems,” IEEE Control Systems Letters, 2 (2), pp. 195–200, 2018.
  • F. Farokhi, I. Shames, N. Batterham, “Secure and Private Control Using Semi-Homomorphic Encryption,” Control Engineering Practice, Vol. 67, pp. 13–20, 2017.

Collaborators: Iman Shames, Carlos Murguia, Vincent Poor, Dragan Nešić, Moritz Schulze Darup, Daniel E. Quevedo, Michael Cantoni

Funding: Defence Science and Technology Group (DSTG), McKenzie Fellowship from The University of Melbourne, National Energy Analytics Research (NEAR) from the Department of the Environment and Energy

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