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Efficient privacy-preserving online medical pre-diagnosis based on blockchain

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Abstract

This paper proposes an online medical pre-diagnosis scheme based on blockchain. It mainly focuses on the problems faced in the medical diagnosis scheme nowadays, such as the huge scale of medical treatment, the lack of data sharing, the impossibility of pursuing responsibility, etc., and constructs a safe and reliable online medical diagnosis scheme by using the characteristics of blockchain technology, such as traceability, decentralization, and openness and transparency. In this paper, the existing homomorphic encryption algorithm is first improved, and the algorithm homomorphism is utilized to generate a public key, which is able to diagnose the data in an encrypted state and ensure the security of the private key. When storing medical data, the original medical data are stored with the help of IPFS file system, and the hash value of medical records is chosen to be stored on the blockchain, which realizes the openness, transparency, and traceability of the data, which can effectively protect the privacy of the data, and also realize the effective pursuit of medical accidents. The specific security analysis, functional comparison, and performance analysis of the scheme further demonstrate that the scheme can safely and efficiently execute online medical pre-diagnosis programs.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work is supported by the National Key Research and Development Program (2018YFA0704703), National Natural Science Foundation of China under Grant (62202375), the Henan Province Science Foundation of Young Scholars under Grant (242300420678), Special project for key R & D and promotion of science and Technology Department of Henan Province (222102210007, 222102210052, 222102210062).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Henan University, Kaifeng, 475004, China

    Sufang Zhou, Jianing Fan, Ke Yuan, Xiaoyu Du & Chunfu Jia

  2. Henan Province Engineering Research Center of Spatial Information Processing, Henan University, Kaifeng, 475004, China

    Sufang Zhou

  3. College of Cybersecurity, Nankai University, Tianjin, 300350, China

    Chunfu Jia

Authors
  1. Sufang Zhou
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  2. Jianing Fan
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  3. Ke Yuan
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  4. Xiaoyu Du
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  5. Chunfu Jia
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Contributions

All authors contributed to the study conception and design. Sufang Zhou performed conceptualization, methodology, formal analysis, funding acquisition, and writing—original draft; Jianing Fan prepared data curation and writing—original draft; Ke Yuan revised writing—review & editing; Chunfu Jia drafted writing—review & editing. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xiaoyu Du.

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Zhou, S., Fan, J., Yuan, K. et al. Efficient privacy-preserving online medical pre-diagnosis based on blockchain. J Supercomput 81, 111 (2025). https://doi.org/10.1007/s11227-024-06486-y

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  • DOI: https://doi.org/10.1007/s11227-024-06486-y

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