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Efficient and Privacy-Preserving Categorization for Encrypted EMR

Author

Listed:
  • Zhiliang Zhao

    (School of Computer and Software Engineering, Xihua University, Chengdu 610039, China
    These authors contributed equally to this work.)

  • Shengke Zeng

    (School of Computer and Software Engineering, Xihua University, Chengdu 610039, China
    These authors contributed equally to this work.)

  • Shuai Cheng

    (School of Computer and Software Engineering, Xihua University, Chengdu 610039, China)

  • Fei Hao

    (School of Computer Science, Shaanxi Normal University, Xi’an 710119, China)

Abstract

Electronic Health Records (EHRs) must be encrypted for patient privacy; however, an encrypted EHR is a challenge for the administrator to categorize. In addition, EHRs are predictable and possible to be guessed, although they are in encryption style. In this work, we propose a secure scheme to support the categorization of encrypted EHRs, according to some keywords. In regard to the predictability of EHRs, we focused on guessing attacks from not only the storage server but also the group administrator. The experiment result shows that our scheme is efficient and practical.

Suggested Citation

  • Zhiliang Zhao & Shengke Zeng & Shuai Cheng & Fei Hao, 2023. "Efficient and Privacy-Preserving Categorization for Encrypted EMR," Mathematics, MDPI, vol. 11(3), pages 1-18, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:754-:d:1055183
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