Abstract
Electronic Health Records (EHRs) play a crucial role in improving healthcare efficiency and quality through data sharing between healthcare institutions and patients. The sharing of EHRs necessitates collaboration, especially in emergency cases where patients require treatment across different institutions. Given that EHRs typically contain sensitive personal information, they require a delicate balance between data collaborative accessibility and privacy protection. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) that supports collaborative decryption facilitates secure, fine-grained, and collaborative access, while maintaining data confidentiality. However, challenges persist in collaborative scenarios, such as identity inference attacks and the hesitancy of healthcare institutions to share EHRs. In this paper, we propose an anonymous collaborative medical data sharing scheme based on blockchain (ACMDS), and introduce an enhanced attribute-based, anonymous collaborative access control approach. Our scheme secures the identity privacy of collaborators by employing the ring signature, and effectively prevents node deception through the mapping established by the smart contract. Security analysis confirms ACMDS’s robustness in preserving data privacy, fostering collaboration, safeguarding identity anonymity, and enabling auditability. Performance evaluations demonstrate ACMDS’s practicality and efficacy in a healthcare context.
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Acknowledgements
This work was supported by the National Key R&D Program of China (2021YFB2700503), the National Natural Science Foundation of China (62071222, 62076125 , 62032025, U21A20467, U20A20176, U22B2030 ), the Shenzhen Science and Technology Program (JCYJ20210324134810028, JCYJ20210324134408023), and the Natural Science Foundation of Jiangsu Province BK20220075.
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Wang, Y., Tao, Y., Wang, H., Ge, C., Zhou, L. (2024). ACMDS: An Anonymous Collaborative Medical Data Sharing Scheme Based on Blockchain. In: Zhang, W., Tung, A., Zheng, Z., Yang, Z., Wang, X., Guo, H. (eds) Web and Big Data. APWeb-WAIM 2024. Lecture Notes in Computer Science, vol 14964. Springer, Singapore. https://doi.org/10.1007/978-981-97-7241-4_17
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