# Bidirectional Information Flow and the Roles of Privacy Masks in Cloud-Based Control

## TL;DR

This paper investigates the interplay of privacy masks and information flow in cloud-based control systems, analyzing how privacy measures affect control performance and proposing joint design strategies for privacy masks.

## Contribution

It introduces a joint design framework for uplink and downlink privacy masks in cloud control, highlighting their non-monotonic impact on privacy and control performance.

## Key findings

- Privacy is not necessarily monotone with noise levels of masks.
- Joint design of uplink and downlink masks improves privacy-control trade-offs.
- Trade-offs between privacy and control performance are quantitatively analyzed.

## Abstract

We consider a cloud-based control architecture for a linear plant with Gaussian process noise, where the state of the plant contains a client's sensitive information. We assume that the cloud tries to estimate the state while executing a designated control algorithm. The mutual information between the client's actual state and the cloud's estimate is adopted as a measure of privacy loss. We discuss the necessity of uplink and downlink privacy masks. After observing that privacy is not necessarily a monotone function of the noise levels of privacy masks, we discuss the joint design procedure for uplink and downlink privacy masks. Finally, the trade-off between privacy and control performance is explored.