Cloud-based MPC with Encrypted Data

TL;DR

This paper develops privacy-preserving cloud-based Model Predictive Control (MPC) protocols using homomorphic encryption, enabling secure control computations for linear systems in IoT settings without revealing private data.

Contribution

It introduces two novel protocols for cloud MPC that ensure privacy using partially homomorphic encryption, suitable for client-server and multi-server architectures.

Findings

Protocols guarantee privacy of client data and control inputs.

Error bounds are derived for encryption-induced inaccuracies.

Numerical simulations demonstrate trade-offs between privacy, communication, and control performance.

Abstract

This paper explores the privacy of cloud outsourced Model Predictive Control (MPC) for a linear system with input constraints. In our cloud-based architecture, a client sends her private states to the cloud who performs the MPC computation and returns the control inputs. In order to guarantee that the cloud can perform this computation without obtaining anything about the client's private data, we employ a partially homomorphic cryptosystem. We propose protocols for two cloud-MPC architectures motivated by the current developments in the Internet of Things: a client-server architecture and a two-server architecture. In the first case, a control input for the system is privately computed by the cloud server, with the assistance of the client. In the second case, the control input is privately computed by two independent, non-colluding servers, with no additional requirements from the client. We prove that the proposed protocols preserve the privacy of the client's data and of the resulting control input. Furthermore, we compute bounds on the errors introduced by encryption. We present numerical simulations for the two architectures and discuss the trade-off between communication, MPC performance and privacy.