A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data
Abstract— Due to the increasing popularity of cloud computing, more and more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-based document retrieval. In this paper, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents. Speciﬁcally, the vector space model and the widely-used < Final Year Projects 2016 > TF_IDF model are combined in the index construction and query generation. We construct a special tree-based 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 ensure accurate relevance score calculation between encrypted index and query vectors. In order to resist statistical attacks, phantom terms are added to the index vector for blinding search results . Due to the use of our special tree-based index structure, the proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents ﬂexibly.
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