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Efficient privacy-preserving online medical primary diagnosis scheme on naive bayesian classification

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Abstract

With the advances of machine learning algorithms and the pervasiveness of network terminals, online medical primary diagnosis scheme, which can provide the primary diagnosis service anywhere anytime, has attracted considerable interest recently. However, the flourish of online medical primary diagnosis scheme still faces many challenges including information security and privacy preservation. In this paper, we propose an efficient and privacy-preserving medical primary diagnosis scheme, called PDiag, on naive Bayes classification. With PDiag, the sensitive personal health information can be processed without privacy disclosure during online medical primary diagnosis service. Specifically, based on an improved expression for the naive Bayes classifier, an efficient and privacy-preserving classification scheme is introduced with lightweight polynomial aggregation technique. The encrypted user query is directly operated at the service provider without decryption, and the diagnosis result can only be decrypted by user. Through extensive analysis, we show that PDiag ensures users’ health information and service provider’s prediction model are kept confidential, and has significantly less computation and communication overhead than existing schemes. In addition, performance evaluations via implementing PDiag on smartphone and computer demonstrate PDiag’s effectiveness in term of real environment.

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Acknowledgments

This work was financially supported by the National Natural Science Foundation of China under Grant 61303218, Grant 6167241 and Grant U1401251, National Key Research and Development Program of China under Grant 2016YFB0800804, Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2016JM6007, Research Foundations for the Central Universities of China under Grant JB161507, and China 111 Project under Grant B16037. We would like to thank the anonymous reviewers for their insightful comments and suggestions.

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Authors and Affiliations

  1. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, 710071, China

    Xiaoxia Liu, Hui Zhu & Hui Li

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang, 639798, Singapore

    Rongxing Lu

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  1. Xiaoxia Liu
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  2. Hui Zhu
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  3. Rongxing Lu
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  4. Hui Li
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Correspondence to Hui Zhu.

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Liu, X., Zhu, H., Lu, R. et al. Efficient privacy-preserving online medical primary diagnosis scheme on naive bayesian classification. Peer-to-Peer Netw. Appl. 11, 334–347 (2018). https://doi.org/10.1007/s12083-016-0506-8

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  • DOI: https://doi.org/10.1007/s12083-016-0506-8

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