EPCS

About

Maintained by
Hui Zhu, Xiaoxia Liu, Rongxing Lu and Hui Li
Xidian University
266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China

Abstract

With the advances of machine learning algorithms and the pervasiveness of network terminals, online medical pre-diagnosis system, which can provide the diagnosis of healthcare provider anywhere anytime, has attracted considerable interest recently. However, the flourish of online medical pre-diagnosis system still faces many challenges including information security and privacy preservation. In this paper, we propose an efficient and privacy-preserving online medical pre-diagnosis framework, called eDiag, by using nonlinear kernel support vector machine (SVM). With eDiag, the sensitive personal health information can be processed without privacy disclosure during online pre-diagnosis service. Specifically, based on an improved expression for the nonlinear SVM, an efficient and privacy-preserving classification scheme is introduced with lightweight multi-party random masking and polynomial aggregation techniques. 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 EDiag can ensure that user's health information and healthcare provider's prediction model are kept confidential, and has significantly less computation and communication overhead than existing schemes. In addition, performance evaluations via implementing eDiag on smartphone and computer demonstrate eDiag's effectiveness in term of real online environment.