EPTDMS: efficient and privacy-preserving top-k disease matching scheme for cloud-assisted e-healthcare system

Abstract

In modern e-healthcare systems, healthcare providers usually store users' data in cloud servers. Users wish to obtain relevant diagnostic files through data generated by body sensors. We propose an efficient and privacy-preserving Top- k disease matching scheme (called EPTDMS). EPTDMS uses Density-Sensitive Hashing (DSH) to implement fuzzy search in stage one, employs the cosine value to sort the relevant result, and obtains patient diagnostic files. Improvements are made to address the problems of low matching efficiency, high computational overhead, and high communication volume of most privacy-preserving matching schemes. This scheme achieves disease matching with low computation and communication overhead and reduces the average query time.