The work describes applying homomorphic encryption and secret sharing to enable privacy-preserving model predictive control on encrypted measurements for cloud-based systems.
A review of homomorphic encryption and software tools for encrypted statistical machine learning
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abstract
Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first decrypt. We review these homomorphic encryption schemes in a manner accessible to statisticians and machine learners, focusing on pertinent limitations inherent in the current state of the art. These limitations restrict the kind of statistics and machine learning algorithms which can be implemented and we review those which have been successfully applied in the literature. Finally, we document a high performance R package implementing a recent homomorphic scheme in a general framework.
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2019 1verdicts
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Secure Multi-party Computation for Cloud-based Control
The work describes applying homomorphic encryption and secret sharing to enable privacy-preserving model predictive control on encrypted measurements for cloud-based systems.