2019/06/23 by Andreea B. Alexandru, George J. Pappas, Alexandru, Andreea B. +1
Computer Science · Engineering · #Cryptography and Data Security #Cryptography and Security (cs.CR) #Distributed systems and fault tolerance #FOS: Computer and information sciences #FOS: Electrical engineering #Privacy-Preserving Technologies in Data #Systems and Control (eess.SY) #cs.CR #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1906.09652
arxiv created 2019/06/23 · openalex publication_date 2019/06/23 · arxiv updated 2019/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.