Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154432
Title: On the relationship between inference and data privacy in decentralized IoT networks
Authors: Sun, Meng
Tay, Wee Peng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Sun, M. & Tay, W. P. (2020). On the relationship between inference and data privacy in decentralized IoT networks. IEEE Transactions On Information Forensics and Security, 15, 852-866. https://dx.doi.org/10.1109/TIFS.2019.2929446
Project: 2017-T1-001-059 (RG20/17)
MOE2018-T2-2-019
Journal: IEEE Transactions on Information Forensics and Security
Abstract: In a decentralized Internet of Things (IoT) network, a fusion center receives information from multiple sensors to infer a public hypothesis of interest. To prevent the fusion center from abusing the sensor information, each sensor sanitizes its local observation using a local privacy mapping, which is designed to achieve both inference privacy of a private hypothesis and data privacy of the sensor raw observations. Various inference and data privacy metrics have been proposed in the literature. We introduce the concept of privacy implication (with vanishing budget) to study the relationships between these privacy metrics. We propose an optimization framework in which both local differential privacy (data privacy) and information privacy (inference privacy) metrics are incorporated. In the parametric case where sensor observations' distributions are known a priori, we propose a two-stage local privacy mapping at each sensor, and show that such an architecture is able to achieve information privacy and local differential privacy to within the predefined budgets. For the nonparametric case where sensor distributions are unknown, we adopt an empirical optimization approach. Simulation and experiment results demonstrate that our proposed approaches allow the fusion center to accurately infer the public hypothesis while protecting both inference and data privacy.
URI: https://hdl.handle.net/10356/154432
ISSN: 1556-6013
DOI: 10.1109/TIFS.2019.2929446
Rights: © 2019 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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