Encrypted Data-driven Predictive Cloud Control with Disturbance Observer

TL;DR

This paper presents a privacy-preserving data-driven predictive cloud control method that uses homomorphic encryption and a disturbance observer to protect data privacy and improve control accuracy in cloud systems.

Contribution

It introduces a novel cloud control scheme combining homomorphic encryption with a disturbance observer for enhanced privacy and robustness.

Findings

Effective privacy protection demonstrated through experiments

Disturbance observer improves control signal accuracy

Cloud-edge cooperation enhances system performance

Abstract

In data-driven predictive cloud control tasks, the privacy of data stored and used in cloud services could be leaked to malicious attackers or curious eavesdroppers. Homomorphic encryption technique could be used to protect data privacy while allowing computation. However, extra errors are introduced by the homomorphic encryption extension to ensure the privacy-preserving properties, and the real number truncation also brings uncertainty. Also, process and measure noise existed in system input and output may bring disturbance. In this work, a data-driven predictive cloud controller is developed based on homomorphic encryption to protect the cloud data privacy. Besides, a disturbance observer is introduced to estimate and compensate the encrypted control signal sequence computed in the cloud. The privacy of data is guaranteed by encryption and experiment results show the effect of our cloud-edge cooperative design.