Time efficient privacy-preserving multi-keyword ranked search over encrypted cloud data

Abstract

Now days for protecting data privacy, sensitive data have to be encrypted before outsourcing, which helps in data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is very important. Data owners are motivated to outsource their data management systems from local sites to the commercial public cloud for great flexibility and economic savings. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. In this paper, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing. Among various multi-keyword semantics, we choose the efficient similarity measure of coordinate matching, i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use inner product similarity to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation to efficiently achieve multi-keyword ranked search and then give two significantly improved MRSE schemes to achieve various privacy requirements.