Efficient privacy‐preserving dot‐product computation for mobile big data

Abstract

Many mobile big data applications require the computation of dot-product of two vectors. For examples, the dot-product of an individual's genome data collected by a body area network and the gene biomarkers of a health centre can help detect diseases in m-Health, and that of the interests of two persons can facilitate profile matching in mobile social networks. Nevertheless, mobile big data typically contain sensitive personal information and are more accessible to the general public as they are collected by mobile devices carried by human beings. Therefore exposing the inputs of dot-product computation discloses sensitive information about the two participants, leading to severe privacy violations. The authors tackle the problem of private dot-product computation targeting mobile big data applications in which secure channels are hardly established, and the computational efficiency is highly desirable. We first propose two basic schemes and then present the corresponding advanced versions to improve computational efficiency and enhance the privacy-protection strength. Furthermore, we theoretically prove that our proposed schemes can simultaneously achieve privacy-preservation, non-repudiation, and accountability. Our numerical results verify the performance of the proposed schemes in terms of communication and computational overheads.