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

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外文摘要：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.

外文关键词：

edge intelligenceblockchainpersonalized privacy preservationdifferential privacySmart Healthcare Networks(SHNs)

作者：

Youyang Qu、Lichuan Ma、Wenjie Ye、Xuemeng Zhai、Shui Yu、Yunfeng Li、David Smith

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作者单位：

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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基金：

National Key Research and Development Program of China

项目编号：

2021YFF0900400

出版年：

2023

DOI：

10.26599/BDMA.2023.9020012

大数据挖掘与分析(英文版)

CSCDEI

ISSN：

年,卷(期)：2023.6(4)

参考文献量45