A Novel Search Scheme on Dynamic Multi-keyword rank over Encrypted Cloud Data

Abstract

A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data Due to the incrementing popularity of cloud computing, more and more data owners are incentivized to outsource their data to cloud servers for great accomodation and reduced cost in data management. However, sensitive data should be encrypted afore outsourcing for privacy requisites, which obsoletes data utilization like keyword-predicated document retrieval. In this paper, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously fortifies dynamic update operations like expunction and insertion of documents. Concretely, the vector space model and the widely-used TF IDF model are cumulated in the index construction and query generation. We construct a special tree-predicated index structure and propose a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search. The secure KNN algorithm is utilized to encrypt the index and query vectors, and meanwhile ascertain precise pertinence score calculation between encrypted index and query vectors. In order to resist statistical attacks, phantom terms are integrated to the index vector for visually impairing search results. Due to the utilization of our special tree-predicated index structure, the proposed scheme can achieve sub-linear search time and deal with the effacement and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.