MPC-Mimicking Neural Network Based on Homomorphic Encryption

This paper showcases the use of homomorphic encryption (HE) scheme for securing process data during the controller evaluation in a simulated untrusted cloud environment. The controller implemented in this work is a neural network (NN) that mimics a model predictive controller (MPC) designed for disturbance rejection. Firstly, an MPC was designed for a process of biochemical reactor. From obtained MPC control data, a neural network (NN-MPC) with fully connected layers was trained. Multiple HE-friendly activation functions were tested during the NN training and testing, and based on the results, a polynomial approximation of hyperbolic tangent was used. Subsequently, the NN-MPC controller was implemented in encrypted control scenario. The measured states of the biochemical reactor were encrypted on the side of the process and sent for the homomorphic evaluation to the simulated cloud (NN-MPC).