Multilayer Nonlinear Processing for Information Privacy in Sensor Networks

TL;DR

This paper introduces a multilayer nonlinear processing method for sensor networks that enhances privacy by distorting data to prevent private hypothesis inference while maintaining public hypothesis detection accuracy.

Contribution

It proposes a novel multilayer nonlinear processing framework with an optimized weighting scheme to balance privacy and utility in sensor data transmission.

Findings

Effective privacy preservation while maintaining public hypothesis detection accuracy.

Optimized weighting matrices improve the privacy-utility trade-off.

Experimental results demonstrate the approach's practical effectiveness.

Abstract

A sensor network wishes to transmit information to a fusion center to allow it to detect a public hypothesis, but at the same time prevent it from inferring a private hypothesis. We propose a multilayer nonlinear processing procedure at each sensor to distort the sensor's data before it is sent to the fusion center. In our proposed framework, sensors are grouped into clusters, and each sensor first applies a nonlinear fusion function on the information it receives from sensors in the same cluster and in a previous layer. A linear weighting matrix is then used to distort the information it sends to sensors in the next layer. We adopt a nonparametric approach and develop a modified mirror descent algorithm to optimize the weighting matrices so as to ensure that the regularized empirical risk of detecting the private hypothesis is above a given privacy threshold, while minimizing the regularized empirical risk of detecting the public hypothesis. Experiments on empirical datasets demonstrate that our approach is able to achieve a good trade-off between the error rates of the public and private hypothesis.