Multilayer Nonlinear Processing for Information Privacy in Sensor Networks
This work addresses privacy protection in sensor networks for applications like surveillance or healthcare, but it is incremental as it builds on existing distortion and optimization methods.
The paper tackles the problem of enabling a sensor network to transmit data for detecting a public hypothesis while preventing inference of a private hypothesis, by proposing a multilayer nonlinear processing procedure that achieves a good trade-off between error rates for public and private hypotheses, as demonstrated in experiments on empirical datasets.
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.