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Blockchain-Based Privacy-Preserving Medical Data Sharing Scheme Using Federated Learning

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Book cover Knowledge Science, Engineering and Management (KSEM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12817))

Abstract

With the booming development of big data technology and health care applications, data in the medical field is characterized by explosive growth, and medical data is valuable, which is the privacy data of patients. However, the characteristics and storage environment of medical big data have brought great challenges to the realization of privacy protection of medical data. In order to ensure the protection of data privacy when sharing medical data, we propose a medical data privacy protection framework based on blockchain (MPBC). In this framework, we protect privacy by adding differential privacy noise into federated learning. In addition, the growing volume of medical data could make blockchain storage problematic. Therefore, a storage mode is proposed to reduce the storage burden of blockchain. The raw data are stored locally and only the hash value calculated by IPFS are stored in blockchain. To enhance the performance, a mechanism is used to validate transactions and aggregate the model. Security analysis shows that our method is a safe and effective way to implement medical data.

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Acknowledgements

The authors would like to thank all anonymous reviewers for their valuable comments and suggestions to improve this paper. This work is partially supported by National Natural Science Foundation of China (Grant No. s 61972034, 61832012, 61771289), Natural Science Foundation of Shandong Province (Grant No. ZR2019ZD10), Natural Science Foundation of Beijing Municipality (Grant No. 4202068), Ministry of Education - China Mobile Research Fund Project (Grant No. MCM20180401).

Author information

Authors and Affiliations

  1. College of Computer Science, Qufu Normal University, Rizhao, 276800, China

    Huiru Zhang & Guangshun Li

  2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Yue Zhang

  3. School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Keke Gai

  4. Texas A&M University-Commerce, Commerce, TX, 75428, USA

    Meikang Qiu

Authors
  1. Huiru Zhang
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  2. Guangshun Li
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  3. Yue Zhang
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  4. Keke Gai
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  5. Meikang Qiu
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Corresponding author

Correspondence to Keke Gai .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Han Qiu

  2. Ibaraki University, Hitachi, Japan

    Cheng Zhang

  3. University of Kentucky, Lexington, KY, USA

    Zongming Fei

  4. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

  5. Princeton University, Princeton, NJ, USA

    Sun-Yuan Kung

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Zhang, H., Li, G., Zhang, Y., Gai, K., Qiu, M. (2021). Blockchain-Based Privacy-Preserving Medical Data Sharing Scheme Using Federated Learning. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_52

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  • DOI: https://doi.org/10.1007/978-3-030-82153-1_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82152-4

  • Online ISBN: 978-3-030-82153-1

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