Privacy-aware decentralized detection using linear precoding
Xin He, Wee Peng Tay, Meng Sun · 2016
We study a nonparametric decentralized detection problem in which sensors send information to a fusion center that uses a support vector machine to make decisions about a public hypothesis. However, the same sensor information may also be used by the fusion center to infer about a private hypothesis, which the sensors wish to protect. To ensure information privacy (as opposed to data privacy), sensors perform linear precoding on their data. We develop an algorithm to optimize the precoder matrices in order to ensure that the empirical risk of detecting the private hypothesis is above a given threshold, while minimizing the empirical regularized risk of detecting the public hypothesis. Simulation results with both synthetic and real data sets demonstrate that our approach is able to ensure information privacy.