Privacy of Information Sharing Schemes in a Cloud-based Multi-sensor Estimation Problem

TL;DR

This paper analyzes privacy levels in multi-sensor cloud-based estimation, comparing local and global sharing schemes, and finds that privacy improves with more sensors, especially under the global scheme.

Contribution

It introduces a formal privacy measure for sensor data sharing in cloud estimation and characterizes privacy bounds for local and global schemes.

Findings

Privacy of local scheme is bounded and tightens with more sensors.

Global scheme becomes asymptotically private as sensors increase.

Privacy loss decreases at rate O(1/M) with more sensors.

Abstract

In this paper, we consider a multi-sensor estimation problem wherein each sensor collects noisy information about its local process, which is only observed by that sensor, and a common process, which is simultaneously observed by all sensors. The objective is to assess the privacy level of (the local process of) each sensor while the common process is estimated using cloud computing technology. The privacy level of a sensor is defined as the conditional entropy of its local process given the shared information with the cloud. Two information sharing schemes are considered: a local scheme, and a global scheme. Under the local scheme, each sensor estimates the common process based on its the measurement and transmits its estimate to a cloud. Under the global scheme, the cloud receives the sum of sensors' measurements. It is shown that, in the local scheme, the privacy level of each sensor is always above a certain level which is characterized using Shannon's mutual information. It is also proved that this result becomes tight as the number of sensors increases. We also show that the global scheme is asymptotically private, i.e., the privacy loss of the global scheme decreases to zero at the rate of O(1/M) where M is the number of sensors.