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

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.