Secure Multi-party Computation for Cloud-based Control

TL;DR

This paper discusses methods for privacy-preserving cloud-based control using multi-party computation and homomorphic encryption, enabling secure control actions on encrypted system data without revealing sensitive information.

Contribution

It introduces a framework for implementing Model Predictive Control securely on encrypted data using cryptographic techniques like homomorphic encryption and secret sharing.

Findings

Secure MPC can be applied to control systems with encrypted data

Homomorphic encryption enables computation on encrypted measurements

Framework ensures privacy without sacrificing control performance

Abstract

In this chapter, we will explore the cloud-outsourced privacy-preserving computation of a controller on encrypted measurements from a (possibly distributed) system, taking into account the challenges introduced by the dynamical nature of the data. The privacy notion used in this work is that of cryptographic multi-party privacy, i.e., the computation of a functionality should not reveal anything more than what can be inferred only from the inputs and outputs of the functionality. The main theoretical concept used towards this goal is Homomorphic Encryption, which allows the evaluation of sums and products on encrypted data, and, when combined with other cryptographic techniques, such as Secret Sharing, results in a powerful tool for solving a wide range of secure multi-party problems. We will rigorously define these concepts and discuss how multi-party privacy can be enforced in the implementation of a Model Predictive Controller, which encompasses computing stabilizing control actions by solving an optimization problem on encrypted data.