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Blockchain-Based Data Preservation System for Medical Data

Overview of attention for article published in Journal of Medical Systems, June 2018
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Title
Blockchain-Based Data Preservation System for Medical Data
Published in
Journal of Medical Systems, June 2018
DOI 10.1007/s10916-018-0997-3
Pubmed ID
Authors

Hongyu Li, Liehuang Zhu, Meng Shen, Feng Gao, Xiaoling Tao, Sheng Liu

Abstract

Medical care has become an indispensable part of people's lives, with a dramatic increase in the volume of medical data (e.g., diagnosis certificates and medical records). Medical data, however, is easily stolen, tampered with, or even completely deleted. If the above occurs, medical data cannot be recorded or retrieved in a reliable manner, resulting in delay treatment progress, even endanger the patient's life. In this paper, we propose a novel blockchain-based data preservation system (DPS) for medical data. To provide a reliable storage solution to ensure the primitiveness and verifiability of stored data while preserving privacy for users, we leverage the blockchain framework. With the proposed DPS, users can preserve important data in perpetuity, and the originality of the data can be verified if tampering is suspected. In addition, we use prudent data storage strategies and a variety of cryptographic algorithms to guarantee user privacy; e.g., an adversary is unable to read the plain text even if the data are stolen. We implement a prototype of the DPS based on the real world blockchain-based platform Ethereum. Performance evaluation results demonstrate the effectiveness and efficiency of the proposed system.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 299 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 299 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 52 17%
Student > Ph. D. Student 41 14%
Researcher 33 11%
Student > Bachelor 16 5%
Student > Doctoral Student 12 4%
Other 38 13%
Unknown 107 36%
Readers by discipline Count As %
Computer Science 96 32%
Engineering 27 9%
Business, Management and Accounting 13 4%
Medicine and Dentistry 10 3%
Social Sciences 9 3%
Other 29 10%
Unknown 115 38%