Efficient and Privacy-Preserving Categorization for Encrypted EMR

Author:

Zhao Zhiliang1,Zeng Shengke1,Cheng Shuai1,Hao Fei2

Affiliation:

1. School of Computer and Software Engineering, Xihua University, Chengdu 610039, China

2. 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.

Funder

Chengdu Science and Technology Program

Sichuan Science and Technology Program

Publisher

MDPI AG

Subject

General Medicine

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