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A Privacy-Preserving Intelligent Medical Diagnosis System Based on Oblivious Keyword Search

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journal contribution
posted on 19.08.2019, 08:36 by Zhaowen Lin, Xinglin Xiao, Yi Sun, Yudong Zhang, Yan Ma
One of the concerns people have is how to get the diagnosis online without privacy being jeopardized. In this paper, we propose a privacy-preserving intelligent medical diagnosis system (IMDS), which can efficiently solve the problem. In IMDS, users submit their health examination parameters to the server in a protected form; this submitting process is based on Paillier cryptosystem and will not reveal any information about their data. And then the server retrieves the most likely disease (or multiple diseases) from the database and returns it to the users. In the above search process, we use the oblivious keyword search (OKS) as a basic framework, which makes the server maintain the computational ability but cannot learn any personal information over the data of users. Besides, this paper also provides a preprocessing method for data stored in the server, to make our protocol more efficient.

Funding

This work is supported by the National High Technology Research and Development Program of China (863 Program) (Grant no. 2013AA014702), the Fundamental Research Funds for the Central Universities (BUPT2016RC48, Grant 2014ZD03-03), and the National Natural Science Foundation of China (Grant no. 61601041).

History

Citation

Mathematical Problems in Engineering, 2017, Article ID 8632183

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics

Version

VoR (Version of Record)

Published in

Mathematical Problems in Engineering

Publisher

Hindawi

issn

1024-123X

eissn

1563-5147

Acceptance date

20/08/2017

Copyright date

2017

Available date

19/08/2019

Publisher version

https://www.hindawi.com/journals/mpe/2017/8632183/

Language

en