Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/107063
Title: Toward information privacy for the internet of things : a nonparametric learning approach
Authors: Sun, Meng
Tay, Wee Peng
He, Xin
Keywords: Information Privacy
Engineering::Electrical and electronic engineering
Decentralized Hypothesis Testing
Issue Date: 2018
Source: Sun, M., Tay, W. P., & He, X. (2018). Toward information privacy for the internet of things : a nonparametric learning approach. IEEE Transactions on Signal Processing, 66(7), 1734-1747. doi:10.1109/TSP.2018.2793871
Series/Report no.: IEEE Transactions on Signal Processing
Abstract: In an Internet of things network, multiple sensors send information to a fusion center for it to infer a public hypothesis of interest. However, the same sensor information may be used by the fusion center to make inferences of a private nature that the sensors wish to protect. To model this, we adopt a decentralized hypothesis testing framework with binary public and private hypotheses. Each sensor makes a private observation and utilizes a local sensor decision rule or privacy mapping to summarize that observation independently of the other sensors. The local decision made by a sensor is then sent to the fusion center.Without assuming knowledge of the joint distribution of the sensor observations and hypotheses, we adopt a nonparametric learning approach to design local privacy mappings. We introduce the concept of an empirical normalized risk, which provides a theoretical guarantee for the network to achieve information privacy for the private hypothesis with high probability when the number of training samples is large. We develop iterative optimization algorithms to determine an appropriate privacy threshold and the best sensor privacy mappings, and show that they converge. Finally, we extend our approach to the case of a private multiple hypothesis. Numerical results on both synthetic and real data sets suggest that our proposed approach yields low error rates for inferring the public hypothesis, but high error rates for detecting the private hypothesis.
URI: https://hdl.handle.net/10356/107063
http://hdl.handle.net/10220/49707
ISSN: 1053-587X
DOI: 10.1109/TSP.2018.2793871
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org.remotexs.ntu.edu.sg/10.1109/TSP.2018.2793871
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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