Cloud-based MPC with Encrypted Data
This addresses privacy concerns for clients outsourcing MPC computations to the cloud, particularly in Internet of Things applications, but it is incremental as it builds on existing encryption methods for specific architectures.
The paper tackles the problem of ensuring privacy in cloud-based Model Predictive Control (MPC) for linear systems with input constraints, proposing protocols using partially homomorphic encryption to compute control inputs without exposing client data, and it demonstrates this with numerical simulations and error bounds.
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.