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On the Relationship Between Inference and Data Privacy in Decentralized IoT Networks
In a decentralized Internet of Things (IoT) network, a fusion center rec...
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Compressive Privacy for a Linear Dynamical System
We consider a linear dynamical system in which the state vector consists...
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Decentralized Detection with Robust Information Privacy Protection
We consider a decentralized detection network whose aim is to infer a pu...
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Arbitrarily Strong Utility-Privacy Trade-off in Decentralized Linear Estimation
Each agent in a network makes a local observation that is linearly relat...
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Energy-efficient Decision Fusion for Distributed Detection in Wireless Sensor Networks
This paper proposes an energy-efficient counting rule for distributed de...
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Information Flow Optimization in Inference Networks
The problem of maximizing the information flow through a sensor network ...
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An Efficient Fog-Assisted Unstable Sensor Detection Scheme with Privacy Preserved
The Internet of Thing (IoT) has been a hot topic in both research commun...
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Multilayer Nonlinear Processing for Information Privacy in Sensor Networks
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.
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