Privacy-Aware Sensor Network Via Multilayer Nonlinear Processing

In Internet of Things, with large amounts of sensor data gathered in fusion center, it is important to detect a public hypothesis, but at the same time it is crucial to prevent a private hypothesis being detected. In order to achieve this goal, a multilayer nonlinear processing procedure is proposed to distort the sensor's data before it is sent to the fusion center. In particular, each sensor applies linear and nonlinear distortions to balance the public hypothesis test and the privacy distortion. Mirror descent methodology is reformulated to optimize the distortion 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 the proposed approach achieves a good tradeoff between the error rates of the public and private hypotheses.