EPDK

About

Maintained by
Dan Zhu, Hui Zhu, Ximeng Liu, Fengwei Wang, and Hao Li
Xidian University
266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China

Abstract

Online medical primary diagnosis system, which can provide the pre-diagnosis service anywhere anytime, has attracted considerable interest. However, the flourish of online medical primary diagnosis system still faces many serious challenges since the sensitivity of personal health information and service provider¡¯s diagnosis model. In this paper, we propose an efficient and privacy-preserving medical primary diagnosis scheme based on K-nearest-neighbors classification (kNN), called EPDK. With EPDK scheme, users can ensure that their sensitive health information are not compromised during the online medical diagnosis process, and service provider can provide high-precise service without revealing its diagnosis model. Specially, based on lightweight multiparty random masking and polynomial aggregation techniques, user preprocesses his/her query vector in ciphertext before sending to the service provider, and the query vector is directly operated in the service provider without obtaining original data, meanwhile, the primary diagnosis result cannot be achieved by anyone except the user. Through extensive analysis, we show that EPDK can resist multifarious known security threats, and has significantly less computation overhead than existing schemes. In addition, performance evaluations via implementing EPDK in the real environment demonstrate that EPDK is highly efficient in terms of computation overhead.