research-article

On the Relationship Between Inference and Data Privacy in Decentralized IoT Networks

Authors:

Wee Peng TayAuthors Info & Claims

IEEE Transactions on Information Forensics and Security, Volume 15

Pages 852 - 866

https://doi.org/10.1109/TIFS.2019.2929446

Published: 01 January 2020 Publication History

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 <italic>a priori</italic>, 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.

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Luo ZAmayri MFan WBouguila N(2023)Cross-collection latent Beta-Liouville allocation model training with privacy protection and applicationsApplied Intelligence10.1007/s10489-022-04378-353:14(17824-17848)Online publication date: 1-Jul-2023
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cover image IEEE Transactions on Information Forensics and Security

IEEE Transactions on Information Forensics and Security Volume 15, Issue

2020

2247 pages

ISSN:1556-6013

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1556-6013 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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Published: 01 January 2020

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Cited By

Jha VTripathi P(2024)Decentralized multiple hypothesis testing in Cognitive IOT using massive heterogeneous dataCluster Computing10.1007/s10586-024-04324-727:5(6889-6929)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s10586-024-04324-7
Zhou JWu NWang YGu SCao ZDong XChoo K(2023)A Differentially Private Federated Learning Model Against Poisoning Attacks in Edge ComputingIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.316855620:3(1941-1958)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TDSC.2022.3168556
Luo ZAmayri MFan WBouguila N(2023)Cross-collection latent Beta-Liouville allocation model training with privacy protection and applicationsApplied Intelligence10.1007/s10489-022-04378-353:14(17824-17848)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s10489-022-04378-3
Lee JLee J(2021)Current Research Trends in IoT SecurityMobile Information Systems10.1155/2021/88470992021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8847099
Sun P(2020)Research on Selection Method of Privacy Parameter εSecurity and Communication Networks10.1155/2020/88450382020Online publication date: 23-Oct-2020
https://dl.acm.org/doi/10.1155/2020/8845038

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