Multilayer Nonlinear Processing for Information Privacy in Sensor Networks
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.
Cite
@article{arxiv.1711.04459,
title = {Multilayer Nonlinear Processing for Information Privacy in Sensor Networks},
author = {Xin He and Meng Sun and Wee Peng Tay and Yi Gong},
journal= {arXiv preprint arXiv:1711.04459},
year = {2018}
}
Comments
The proof in Theorem 1 relies on the proof from other papers, but the extension from the discrete space can not be directly extended to the continuous space. Therefore, the proof in Theorem 1 is not reliable. The third author is responsible for the correctness in Section II.B, while the first author is responsible for other sections