Paper Infomation
Research on Secure Circulation Methods for Health Big Data: A Comprehensive Survey
Full Text(PDF, 913KB)
Author: Jin Yi, Zhou Zhou, Jiong Wang
Abstract: With electronic health records, wearables, and genomic sequencing now generating massive amounts of health data, researchers are increasingly able to advance precision medicine and population health management. However, putting this data to work is complicated by strict privacy regulations like HIPAA and GDPR, which make it difficult to share information across hospitals or regions. In this survey, we review how researchers have tackled these security challenges from 2020 to 2025. We organize the current solutions into four main groups: cryptographic tools (like attribute-based and homomorphic encryption), blockchain-based networks, privacy-preserving AI methods (such as federated and swarm learning), and dynamic access controls. For each area, we look at how they work, what security they provide, and whether they are ready for real-world use. We also examine new trends in health data markets and interoperability standards. Our analysis shows that most methods still struggle to balance data usefulness with privacy protection. Finally, we point out key open problems, particularly around scaling up to large networks, standardizing data formats, and making blockchain audits both transparent and confidential.
Keywords: Health Big Data; Circulation; Electronic Health Records; Blockchain
References:
[1] N. Rieke, J. Hancox, W. Li, F. Milletari, H. R. Roth, S. Albarqouni, et al., “The future of digital health with federated learning,” npj Digital Medicine, vol. 3, no. 1, p. 119, 2020.
[2] J. Acosta, G. J. Falcone, P. Rajpurkar, and E. J. Topol, “Multimodal biomedical AI,” Nature Medicine, vol. 28, no. 9, pp. 1773–1784, 2022.
[3] J. Thomason, “Big tech, big data and the new world of digital health,” Global Health Journal, vol. 5, no. 4, pp. 165–168, 2021.
[4] J. Xu, B. S. Glicksberg, C. Su, P. Walker, J. Bian, and F. Wang, “Federated learning for healthcare informat- ics,” Journal of Healthcare Informatics Research, vol. 5, pp. 1–19, 2020.
[5] Dayan, H. R. Roth, A. Zhong, A. Harouni, A. Gentili, A. Abidin, et al., “Federated learning for predicting clinical outcomes in patients with COVID-19,” Nature Medicine, vol. 27, no. 10, pp. 1735–1743, 2021.
[6] J. Budd, B. S. Miller, E. Manning, V. Lampos, M. Zhuang, M. Edelstein, et al., “Digital technologies in the public-health response to COVID-19,” Nature Medicine, vol. 26, no. 8, pp. 1183–1192, 2020.
[7] G. Kaissis, M. R. Makowski, D. Rückert, and R. Braren, “Secure, privacy-preserving and federated machine learning in medical imaging,” Nature Machine Intelligence, vol. 2, no. 6, pp. 305–311, 2020.
[8] A. Ghubaish, T. Salman, M. Zolanvari, D. Ünal, A. Al-Ali, and R. Jain, “Recent advances in the Internet-of- Medical-Things (IoMT) systems security,” IEEE Internet of Things Journal, vol. 8, no. 11, pp. 8707–8718, 2020.
[9] L. Tan, K. Yu, N. Shi, C. Yang, and W. Wei, “Towards secure and privacy-preserving data sharing for COVID-19 medical records: A blockchain-empowered approach,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 1, pp. 271–281, 2021.
[10] X. Yin, Y. Zhu, and J. Hu, “A comprehensive survey of privacy-preserving federated learning,” ACM Com- puting Surveys, vol. 54, no. 6, pp. 1–36, 2021.
[11] H. Hu, Z. Salcic, L. Sun, G. Dobbie, P. S. Yu, and X. Zhang, “Membership inference attacks on machine learning: A survey,” ACM Computing Surveys, vol. 55, no. 2, pp. 1–37, 2022.
[12] G. Kaissis, A. Ziller, J. Passerat-Palmbach, T. Ryffel, D. Usynin, A. Trask, et al., “End-to-end privacy preserving deep learning on multi-institutional medical imaging,” Nature Machine Intelligence, vol. 3, no. 6, pp. 473–484, 2021.
[13] M. Sheller, B. Edwards, G. A. Reina, J. Martin, S. Pati, A. Kotrotsou, et al., “Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data,” Scientific Reports, vol. 10, no. 1, p. 12598, 2020.
[14] S. Warnat-Herresthal, H. Schultze, K. L. Shastry, S. Manamohan, S. Mukherjee, V. Garg, et al., “Swarm learning for decentralized and confidential clinical machine learning,” Nature, vol. 594, no. 7862, pp. 265– 270, 2021.
[15] A. Tandon, A. Dhir, A. K. M. N. Islam, and M. Mäntymäki, “Blockchain in healthcare: A systematic lit- erature review, synthesizing framework and future research agenda,” Computers in Industry, vol. 122, p. 103290, 2020.
[16] A. Dubovitskaya, F. Baig, Z. Xu, R. Shukla, P. S. Zambani, A. Swaminathan, et al., “ACTION-EHR: Patient- centric blockchain-based electronic health record data management for cancer care,” Journal of Medical Internet Research, vol. 22, no. 8, p. e13598, 2020.
[17] R. Akkaoui, X. Hei, and W. Cheng, “An evolutionary game-theoretic trust study of a blockchain-based personal health data sharing framework,” in Proc. ICTC, 2020, pp. 130–135.
[18] F. I. Anik, N. Sakib, H. Shahriar, Y. Xie, and H. A. Nahiyan, “Unraveling a blockchain-based framework towards patient empowerment: A scoping review,” Smart Health, vol. 28, p. 100401, 2023.
[19] R. G. Sonkamble, A. M. Bongale, S. Phansalkar, A. Sharma, and S. Rajput, “Secure data transmission of electronic health records using blockchain technology,” Electronics, vol. 12, no. 4, p. 1015, 2023.
[20] F. A. Reegu, H. Abas, Y. Gulzar, Q. Xin, A. A. Alwan, A. Jabbari, et al., “Blockchain-based framework for interoperable electronic health records,” Sustainability, vol. 15, no. 8, p. 6337, 2023.
[21] A. Ali, M. A. Almaiah, F. Hajjej, M. F. Pasha, O. H. Fang, R. Khan, et al., “An industrial IoT-based blockchain-enabled secure searchable encryption approach for healthcare,” Sensors, vol. 22, no. 2, p. 572, 2022.
[22] P. E. Velmovitsky, F. M. Bublitz, L. Fadrique, and P. P. Morita, “Blockchain applications in health care and public health: Increased transparency,” JMIR Medical Informatics, vol. 9, no. 4, p. e20713, 2021.
[23] M. A. Maher, I. Khan, and V. Prikshat, “Monetisation of digital health data through a GDPR-compliant and blockchain enabled digital health data marketplace,” International Journal of Information Management Data Insights, vol. 3, no. 1, p. 100159, 2023.
[24] J. Zhang and Z. Zhang, “Ethics and governance of trustworthy medical artificial intelligence,” BMC Medical Informatics and Decision Making, vol. 23, no. 1, p. 402, 2023.
[25] S. Williamson and V. R. Prybutok, “Balancing privacy and progress: A review of privacy challenges in AI-driven healthcare,” Applied Sciences, vol. 14, no. 2, p. 675, 2024.
[26] A. F. Markus, J. A. Kors, and P. R. Rijnbeek, “The role of explainability in creating trustworthy artificial in- telligence for health care: A comprehensive survey,” Journal of Biomedical Informatics, vol. 110, p. 103655, 2020.
[27] C. Wu, F. Wu, L. Lyu, Y. Huang, and X. Xie, “Communication-efficient federated learning via knowledge distillation,” Nature Communications, vol. 13, no. 1, p. 2032, 2022.
[28] A. Z. Tan, H. Yu, L. Cui, and Q. Yang, “Towards personalized federated learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 12, pp. 9587–9603, 2022.
[29] L. Slawomirski, L. Lindner, K. De Bienassis, P. Haywood, and T. C. O. Hashiguchi, “Progress on imple- menting and using electronic health record systems,” OECD Health Working Papers, no. 157, 2023.
[30] D. Taliun, D. Harris, M. D. Kessler, J. Carlson, Z. A. Szpiech, R. Torres, et al., “Sequencing of 53,831 diverse genomes from the NHLBI TOPMed program,” Nature, vol. 590, no. 7845, pp. 290–299, 2021.
[31] K. Bayoumy, M. Gaber, A. Elshafeey, O. Mhaimeed, E. H. Dineen, F. A. Marvel, et al., “Smart wearable devices in cardiovascular care,” Nature Reviews Cardiology, vol. 18, no. 8, pp. 581–599, 2021.
[32] S. F. Ahmed, M. S. B. Alam, S. Afrin, S. J. Rafa, N. Rafa, and A. H. Gandomi, “Insights into Internet of Medical Things (IoMT): Data fusion, security issues and potential solutions,” Information Fusion, vol. 102, p. 102060, 2023.
[33] O. Popoola, M. Rodrigues, J. Marchang, A. Shenfield, and A. Ikpehai, “A critical literature review of security and privacy in smart home healthcare schemes adopting IoT and blockchain,” Blockchain: Research and Applications, vol. 4, no. 3, p. 100178, 2023.