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PPCD: Privacy-preserving clinical decision with cloud support

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  • Hui Ma
  • Xuyang Guo
  • Yuan Ping
  • Baocang Wang
  • Yuehua Yang
  • Zhili Zhang
  • Jingxian Zhou

Abstract

With the prosperity of machine learning and cloud computing, meaningful information can be mined from mass electronic medical data which help physicians make proper disease diagnosis for patients. However, using medical data and disease information of patients frequently raise privacy concerns. In this paper, based on single-layer perceptron, we propose a scheme of privacy-preserving clinical decision with cloud support (PPCD), which securely conducts disease model training and prediction for the patient. Each party learns nothing about the other’s private information. In PPCD, a lightweight secure multiplication is presented and introduced to improve the model training. Security analysis and experimental results on real data confirm the high accuracy of disease prediction achieved by the proposed PPCD without the risk of privacy disclosure.

Suggested Citation

  • Hui Ma & Xuyang Guo & Yuan Ping & Baocang Wang & Yuehua Yang & Zhili Zhang & Jingxian Zhou, 2019. "PPCD: Privacy-preserving clinical decision with cloud support," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-17, May.
    • Handle: RePEc:plo:pone00:0217349
    DOI: 10.1371/journal.pone.0217349
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    References listed on IDEAS

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    1. Fabian Taigel & Anselme K. Tueno & Richard Pibernik, 2018. "Privacy-preserving condition-based forecasting using machine learning," Journal of Business Economics, Springer, vol. 88(5), pages 563-592, July.
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    Cited by:

    1. Siala, Haytham & Wang, Yichuan, 2022. "SHIFTing artificial intelligence to be responsible in healthcare: A systematic review," Social Science & Medicine, Elsevier, vol. 296(C).

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