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Open Access

Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence

Data61, Commonwealth Scientific and Industrial Research Organization (CSIRO), Sydney 2015, Australia
School of Cyber Engineering, Xidian University, Xi’an 710126, China
College of Engineering and Science, Victoria University, Melbourne 3000, Australia
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
School of Computer Science, University of Technology Sydney, Sydney 2007, Australia
CNPIEC KEXIN LTD., Beijing 100020, China
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Abstract

The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks (SHNs). To enhance the precision of diagnosis, different participants in SHNs share health data that contain sensitive information. Therefore, the data exchange process raises privacy concerns, especially when the integration of health data from multiple sources (linkage attack) results in further leakage. Linkage attack is a type of dominant attack in the privacy domain, which can leverage various data sources for private data mining. Furthermore, adversaries launch poisoning attacks to falsify the health data, which leads to misdiagnosing or even physical damage. To protect private health data, we propose a personalized differential privacy model based on the trust levels among users. The trust is evaluated by a defined community density, while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential privacy. To avoid linkage attacks in personalized differential privacy, we design a noise correlation decoupling mechanism using a Markov stochastic process. In addition, we build the community model on a blockchain, which can mitigate the risk of poisoning attacks during differentially private data transmission over SHNs. Extensive experiments and analysis on real-world datasets have testified the proposed model, and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.

Keywords

edge intelligenceblockchainpersonalized privacy preservationdifferential privacySmart Healthcare Networks (SHNs)

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Big Data Mining and Analytics
Volume 6 Issue 4,
December 2023
Pages 443-464
DOI: 10.26599/BDMA.2023.9020012
Cite this article:
Qu Y, Ma L, Ye W, et al. Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence. Big Data Mining and Analytics, 2023, 6(4): 443-464. https://doi.org/10.26599/BDMA.2023.9020012

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Received: 06 December 2022
Revised: 30 May 2023
Accepted: 04 June 2023
Published: 29 August 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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