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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)

References

[1]
D. Garcia, Leaking privacy and shadow profiles in online social networks, Sci. Adv., vol. 3, no. 8, p. e1701172, 2017.
Crossref Google Scholar
[2]
M. S. Hossain, C. Xu, Y. Li, J. Bilbao, and A. El-Saddik, Advances in next-generation networking technologies for smart healthcare, IEEE Commun. Mag., vol. 56, no. 4, pp. 14–15, 2018.
Crossref Google Scholar
[3]
Y. Wang, A. Zhang, P. Zhang, Y. Qu, and S. Yu, Security-aware and privacy-preserving personal health record sharing using consortium blockchain, IEEE Internet Things J., vol. 9, no. 14, pp. 12014–12028, 2022.
Crossref Google Scholar
[4]
X. Zhou, X. Liang, H. Zhang, and Y. Ma, Cross-platform identification of anonymous identical users in multiple social media networks, IEEE Trans. Knowl. Data Eng., vol. 28, no. 2, pp. 411–424, 2016.
Crossref Google Scholar
[5]
L. Catarinucci, D. De Donno, L. Mainetti, L. Palano, L. Patrono, M. L. Stefanizzi, and L. Tarricone, An IoT-aware architecture for smart healthcare systems, IEEE Internet Things J., vol. 2, no. 6, pp. 515–526, 2015.
Crossref Google Scholar
[6]
S. Yu, Big privacy: Challenges and opportunities of privacy study in the age of big data, IEEE Access, vol. 4, pp. 2751–2763, 2016.
Crossref Google Scholar
[7]
Y. Qu, S. Yu, W. Zhou, S. Peng, G. Wang, and K. Xiao, Privacy of things: Emerging challenges and opportunities in wireless internet of things, IEEE Wirel. Commun., vol. 25, no. 6, pp. 91–97, 2018.
Crossref Google Scholar
[8]
D. He, R. Ye, S. Chan, M. Guizani, and Y. Xu, Privacy in the internet of things for smart healthcare, IEEE Commun. Mag., vol. 56, no. 4, pp. 38–44, 2018.
Crossref Google Scholar
[9]
X. Nie, A. Zhang, J. Chen, Y. Qu, and S. Yu, Blockchain-empowered secure and privacy-preserving health data sharing in edge-based IoMT, Security and Communication Networks, vol. 2022, p. 8293716, 2022.
Crossref Google Scholar
[10]
Y. Zhang, J. Tang, Z. Yang, J. Pei, and P. S. Yu, COSNET: Connecting heterogeneous social networks with local and global consistency, in Proc. 21th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Sydney, Australia, 2015, pp. 1485–1494.
Crossref Google Scholar
[11]
J. Zhang, Q. Zhan, and P. S. Yu, Concurrent alignment of multiple anonymized social networks with generic stable matching, in Theoretical Information Reuse and Integration, T. Bouabana-Tebibel and S. H. Rubin, eds. Switzerland: Springer, 2016, pp. 173–196.
Crossref
[12]
M. M. Merener, Theoretical results on de-anonymization via linkage attacks, Trans. Data Privacy, vol. 5, no. 2, pp. 377–402, 2012.
Google Scholar
[13]
Y. Gong, C. Zhang, Y. Fang, and J. Sun, Protecting location privacy for task allocation in ad hoc mobile cloud computing, IEEE Trans. Emerg. Topics Comput., vol. 6, no. 1, pp. 110–121, 2018.
Crossref Google Scholar
[14]
P. Samarati and L. Sweeney, Protecting privacy when disclosing information: K-anonymity and its enforcement through generalization and suppression, in Proceedings of the IEEE Symposium on Research in Security and Privacy, Oakland, CA, USA, 1998, pp. 1–19.
Google Scholar
[15]
A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, L-diversity: Privacy beyond k-anonymity, ACM Trans. Knowl. Discov. Data, vol. 1, no. 1, pp. 3–55, 2007.
Crossref Google Scholar
[16]
N. Li, T. Li, and S. Venkatasubramanian, Closeness: A new privacy measure for data publishing, IEEE Trans. Knowl. Data Eng., vol. 22, no. 7, pp. 943–956, 2010.
Crossref Google Scholar
[17]
C. Dwork, Differential privacy, in Proc. 33rd Int. Conf. Automata, Languages and Programming – Volume Part II, Venice, Italy, 2006, pp. 1–12.
Crossref Google Scholar
[18]
C. Dwork, Differential privacy, in Encyclopedia of Cryptography and Security, 2nd ed., H. C. A. van Tilborg and S. Jajodia, eds. New York, NY, USA: Springer, 2011, pp. 338–340.
Crossref
[19]
F. Koufogiannis and G. J. Pappas, Diffusing private data over networks, IEEE Trans. Control Netw. Syst., vol. 5, no. 3, pp. 1027–1037, 2018.
Crossref Google Scholar
[20]
S. Wang, J. Wang, X. Wang, T. Qiu, Y. Yuan, L. Ouyang, Y. Guo, and F. Wang, Blockchain-powered parallel healthcare systems based on the ACP approach, IEEE Trans. Comput. Soc. Syst., vol. 5, no. 4, pp. 942–950, 2018.
Crossref Google Scholar
[21]
Y. Y. Ahn, J. P. Bagrow, and S. Lehmann, Link communities reveal multiscale complexity in networks, Nature, vol. 466, no. 7307, pp. 761–764, 2010.
Crossref Google Scholar
[22]
Y. Qu, L. Gao, Y. Xiang, S. Shen, and S. Yu, Fedtwin: Blockchain-enabled adaptive asynchronous federated learning for digital twin networks, IEEE Netw., vol. 36, no. 6, pp. 183–190, 2022.
Crossref Google Scholar
[23]
W. Wang and Q. Zhang, Privacy preservation for context sensing on smartphone, IEEE/ACM Trans. Netw., vol. 24, no. 6, pp. 3235–3247, 2016.
Crossref Google Scholar
[24]
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor, Our data, ourselves: Privacy via distributed noise generation, in Proc. 24th Annu. Int. Conf. Theory and Applications of Cryptographic Techniques, St. Petersburg, Russia, 2006, pp. 486–503.
Crossref Google Scholar
[25]
S. R. Pokhrel, Y. Qu, and L. Gao, QoS-aware personalized privacy with multipath TCP for industrial IoT: Analysis and design, IEEE Internet Things J., vol. 7, no. 6, pp. 4849–4861, 2020.
Crossref Google Scholar
[26]
J. Ma, Y. Qiao, G. Hu, Y. Huang, M. Wang, A. K. Sangaiah, C. Zhang, and Y. Wang, Balancing user profile and social network structure for anchor link inferring across multiple online social networks, IEEE Access, vol. 5, pp. 12031–12040, 2017.
Crossref Google Scholar
[27]
S. R. Pokhrel, Y. Qu, S. Nepal, and S. Singh, Privacy-aware autonomous valet parking: Towards experience driven approach, IEEE Trans. Intell. Transp. Syst., vol. 22, no. 8, pp. 5352–5363, 2021.
Crossref Google Scholar
[28]
D. Perito, C. Castelluccia, M. A. Kaafar, and P. Manils, How unique and traceable are usernames? in Proc. 11th Int. Symp. Privacy Enhancing Technologies, Waterloo, Canada, 2011, pp. 1–17.
Crossref Google Scholar
[29]
R. Zafarani and H. Liu, Connecting users across social media sites: A behavioral-modeling approach, in Proc. 19th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Chicago, IL, USA, 2013, pp. 41–49.
Crossref Google Scholar
[30]
K. Xu, Y. Guo, L. Guo, Y. Fang, and X. Li, My privacy my decision: Control of photo sharing on online social networks, IEEE Transactions on Dependable and Secure Computing, vol. 14, no. 2, pp. 199–210, 2017.
Crossref Google Scholar
[31]
R. Schlegel, C. Chow, Q. Huang, and D. S. Wong, Privacy-preserving location sharing services for social networks, IEEE Trans. Serv. Comput., vol. 10, no. 5, pp. 811–825, 2017.
Crossref Google Scholar
[32]
J. A. Miller and B. Hoover, An exploratory analysis of the effects of spatial and temporal scale and transportation mode on anonymity in human mobility trajectories, in Human Dynamics Research in Smart and Connected Communities, S. L. Shaw and D. Sui, eds. New York, NY, USA: Springer, 2018, pp. 149–162.
Crossref
[33]
H. Li, Q. Chen, H. Zhu, D. Ma, H. Wen, and X. Shen, Privacy leakage via de-anonymization and aggregation in heterogeneous social networks, IEEE Transactions on Dependable and Secure Computing, vol. 17, no. 2, pp. 350–362, 2017.
Crossref Google Scholar
[34]
Y. Yang, X. Zheng, W. Guo, X. Liu, and V. Chang, Privacy-preserving smart IoT-based healthcare big data storage and self-adaptive access control system, Inf. Sci., vol. 479, pp. 567–592, 2019.
Crossref Google Scholar
[35]
Y. Zhang, D. Zheng, and R. H. Deng, Security and privacy in smart health: Efficient policy-hiding attribute based access control, IEEE Internet Things J., vol. 5, no. 3, pp. 2130–2145, 2018.
Crossref Google Scholar
[36]
H. Liu, X. Yao, T. Yang, and H. Ning, Cooperative privacy preservation for wearable devices in hybrid computing-based smart health, IEEE Internet Things J., vol. 6, no. 2, pp. 1352–1362, 2019.
Crossref Google Scholar
[37]
Y. Qu, M. P. Uddin, C. Gan, Y. Xiang, L. Gao, and J. Yearwood, Blockchain-enabled federated learning: A survey, ACM Comput. Surv., vol. 55, no. 4, p. 70, 2022.
Crossref Google Scholar
[38]
J. Xu, K. Xue, S. Li, H. Tian, J. Hong, P. Hong, and N. Yu, Healthchain: A blockchain-based privacy preserving scheme for large-scale health data, IEEE Internet Things J., vol. 6, no. 5, pp. 8770–8781, 2019.
Crossref Google Scholar
[39]
A. D. Dwivedi, G. Srivastava, S. Dhar, and R. Singh, A decentralized privacy-preserving healthcare blockchain for IoT, Sensors, vol. 19, no. 2, p. 326, 2019.
Crossref Google Scholar
[40]
K. J. Peterson, R. Deeduvanu, P. Kanjamala, and K. Boles, A blockchain-based approach to health information exchange networks, in Proc. NIST Workshop Blockchain Healthcare, Miami, FL, USA, 2016, pp. 1–10.
Google Scholar
[41]
H. D. Zubaydi, Y. W. Chong, K. Ko, S. M. Hanshi, and S. Karuppayah, A review on the role of blockchain technology in the healthcare domain, Electronics, vol. 8, no. 6, p. 679, 2019.
Crossref Google Scholar
[42]
Y. Qu, X. Yuan, M. Ding, W. Ni, T. Rakotoarivelo, and D. Smith, Learn to unlearn: A survey on machine unlearning, arXiv preprint arXiv: 2305.07512, 2023.
Google Scholar
[43]
Doximity, Doximity developer API, https://www.doximity.com/, 2022.
[44]
HealthTap, Healthtap developer API, https://www.healthtap.com/, 2022.
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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