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Privacy-Preserving Authenticated Federated Learning Scheme for Smart Healthcare System

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Emerging Information Security and Applications (EISA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2004 ))

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Abstract

With the rapid advancement of artificial intelligence and network technology, smart healthcare system provides patients with satisfactory medical experience and clinical diagnosis, thus alleviating the imbalance between limited medical resources and a large patient population. However, the patient privacy security in smart healthcare system is still facing severe challenges. Additionally, due to the signal interruption in the federated learning mechanism, the model parameters can not be transmitted normally between local user and central server. In response to these issues, we propose a privacy-preserving authenticated federated learning scheme for smart healthcare system. Specifically, there is a hybrid federated learning framework composed of peer-to-peer and server-client architecture in the proposed scheme. In the proposed scheme, data owners can interact directly with each other for federated training to overcome the data silos issue. In addition, we leverage a homomorphic cryptosystem and the Schnorr signature algorithm to ensure the security and integrity of local model parameters. Security analysis and experimental results show that the proposed scheme can not only protect the sensitive information of data owners, but also has high efficiency.

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Acknowledgements

This work supported in part by the Major Research Plan of Hubei Province under Grant/Award No. 2023BAA027, and the National Natural Science Foundation of China under grants 62072134, U2001205, and the key Research and Development Program of Hubei Province under Grant 2021BEA163. In addition, Jun Tu would like to express gratitude to his classmate Yifan Liu for his support in providing the experimental conditions.

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Authors and Affiliations

  1. School of Computer Science, Hubei University of Technology, Wuhan, 430068, China

    Jun Tu & Gang Shen

Authors
  1. Jun Tu
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  2. Gang Shen
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Correspondence to Gang Shen .

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Editors and Affiliations

  1. Zhejiang Gongshang University, Hangzhou, China

    Jun Shao

  2. Norwegian University of Science and Technology, Gjøvik, Norway

    Sokratis K. Katsikas

  3. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

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Tu, J., Shen, G. (2024). Privacy-Preserving Authenticated Federated Learning Scheme for Smart Healthcare System. In: Shao, J., Katsikas, S.K., Meng, W. (eds) Emerging Information Security and Applications. EISA 2023. Communications in Computer and Information Science, vol 2004 . Springer, Singapore. https://doi.org/10.1007/978-981-99-9614-8_3

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  • DOI: https://doi.org/10.1007/978-981-99-9614-8_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9613-1

  • Online ISBN: 978-981-99-9614-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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