PGuide: An Efficient and Privacy-Preserving Smartphone-Based Pre-Clinical Guidance Scheme

Abstract

With the pervasiveness of smartphones, mobile e-Healthcare has attracted considerable attention in recent years. Disease risk prediction, as it can assist in predicting user's disease with big data analytics techniques, has become one of important topics in the field of e-Healthcare. However, if the privacy issue is not well addressed, disease risk predication cannot step into its flourish. Aiming at addressing this challenge, in this paper, we propose a new efficient and privacy- preserving pre-clinical guidance scheme, called PGuide, which offers self-diagnosis service to medical users in a privacy-preserving way. In specific, to motivate medical users to provide more detailed health profile for accurate disease risk prediction, we introduce a privacy-preserving comparison protocol PPCP in the PGuide scheme. As a result, with enough health profile information offered by the medical users, the accuracy of disease risk prediction can be improved. Detailed security analysis shows that our proposed PGuide scheme ensures the privacy-preservation for both medical users and service provider. In addition, the performance evaluation via extensive experiments also demonstrates that our proposed PPCP protocol is much efficient in terms of low computational cost and communication overhead.