2017/11/13 by Xin He, Meng Sun, He, Xin +5
Computer Science · #Adversarial Robustness in Machine Learning #Cryptography and Security (cs.CR) #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #FOS: Electrical engineering #Signal Processing (eess.SP) #Target Tracking and Data Fusion in Sensor Networks #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1711.04459
openalex publication_date 2017/11/13 · openalex created_date 2017/12/04 · openalex updated_date 2026/07/28
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.