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MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY

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Citation Author(s):
Xin He, Wee Peng Tay
Submitted by:
Xin He
Last updated:
1 March 2017 - 1:57am
Document Type:
Poster
Document Year:
2017
Event:
Presenters Name:
Xin He, Wee Peng Tay
Paper Code:
IOT-P1.7

Abstract 

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 sensor network structure, where each sensor first applies a nonlinear fusion function on the information it receives from sensors in a previous layer, and then a linear weighting matrix to distort the information it sends to sensors in the next layer. We adopt a nonparametric approach and develop an 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. Simulations on a synthetic dataset and an empirical experiment demonstrate that our approach is able to achieve a better trade-off between the error rates of the public and private hypothesis than using only linear precoding to achieve information privacy.

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