Private outsourced model predictive control via secure multi-party computation

Protecting data privacy is essential in developing practical cloud -based control systems to benefit from remote and distributed computation while avoiding the risks of disclosing sensitive information to potentially untrusted parties. This paper proposes a privacy -preserving framework to outsource the computation of model predictive control law to several untrusted cloud -based servers. A secret sharing scheme is employed to maintain the privacy of the system's data in all the stages of the control loop in a secure multi -party computation environment. In this regard, a privacy -preserving algorithm is derived to securely implement a projected gradient method to solve the underlying optimization problem, without revealing private data to external eavesdroppers and curious cloud -based servers. The proposed method is solely based on secret sharing, and all computations can be performed securely by cloud servers without the need to designate target nodes or engage the system's actuator in computing the intermediate steps. Therefore, the proposed method is less computationally demanding than the existing results based on homomorphic encryption and is applicable to systems with limited computing resources. Information -theoretic privacy assessments based on mutual information measures are provided. The efficacy of the proposed method is investigated on the cruise control problem of a freight train system.