A Novel Algorithm for Inferring user Search Multi-Keyword Ranked over Encrypted on Multi Cloud

The arrival of cloud computing, data owners are motivated to subcontract their complex data management systems from local sites to commercial public cloud for great elasticity and economic savings. But for protecting data privacy, sensitive data has to be encrypted before outsourcing, Considering the large number of data users and documents in cloud, it is key for the search service to allow multi-keyword query and provide result similarity ranking to meet the effective data retrieval need. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. Among various multi-keyword semantics, we choose the efficient principle of “coordinate matching”. We first propose a basic MRSE scheme using secure inner product computation, and experiments on the real-world dataset further show proposed schemes indeed introduce low overhead on computation and communication. We propose a novel optimization method to map feedback sessions to pseudo (fake)-documents which can efficiently reflect user information needs. We also propose a novel evaluation criterion classified average precision (CAP) to evaluate the performance of the restructured web search results. To improve search experience of the data search service, we further extend these two schemes to support more search semantics. Experiments on the real-world data set further show proposed schemes indeed introduce low overhead on computation and communication.