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

Zhaowen Lin, Xinglin Xiao, Yi Sun, Yudong Zhang and Yan Ma

Mathematical Problems in Engineering, 2017, vol. 2017, 1-7

Abstract:

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8632183

DOI: 10.1155/2017/8632183

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