Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence
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 exchan...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020012 |
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| author | Youyang Qu Lichuan Ma Wenjie Ye Xuemeng Zhai Shui Yu Yunfeng Li David Smith |
| author_facet | Youyang Qu Lichuan Ma Wenjie Ye Xuemeng Zhai Shui Yu Yunfeng Li David Smith |
| author_sort | Youyang Qu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-444f3350745a4fba8aafbbdffb47001d |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-444f3350745a4fba8aafbbdffb47001d2025-02-03T07:59:13ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-12-016444346410.26599/BDMA.2023.9020012Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge IntelligenceYouyang Qu0Lichuan Ma1Wenjie Ye2Xuemeng Zhai3Shui Yu4Yunfeng Li5David Smith6Data61, Commonwealth Scientific and Industrial Research Organization (CSIRO), Sydney 2015, AustraliaSchool of Cyber Engineering, Xidian University, Xi’an 710126, ChinaCollege of Engineering and Science, Victoria University, Melbourne 3000, AustraliaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Computer Science, University of Technology Sydney, Sydney 2007, AustraliaCNPIEC KEXIN LTD., Beijing 100020, ChinaData61, Commonwealth Scientific and Industrial Research Organization (CSIRO), Sydney 2015, AustraliaThe 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.https://www.sciopen.com/article/10.26599/BDMA.2023.9020012edge intelligenceblockchainpersonalized privacy preservationdifferential privacysmart healthcare networks (shns) |
| spellingShingle | Youyang Qu Lichuan Ma Wenjie Ye Xuemeng Zhai Shui Yu Yunfeng Li David Smith Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence Big Data Mining and Analytics edge intelligence blockchain personalized privacy preservation differential privacy smart healthcare networks (shns) |
| title | Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence |
| title_full | Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence |
| title_fullStr | Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence |
| title_full_unstemmed | Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence |
| title_short | Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence |
| title_sort | towards privacy aware and trustworthy data sharing using blockchain for edge intelligence |
| topic | edge intelligence blockchain personalized privacy preservation differential privacy smart healthcare networks (shns) |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020012 |
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