Multikeyword Rank Search Scheme for Unindexed Encrypted Cloud Data

The increasing popularity of cloud computing leads to more and more data owners 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 no longer support data utilization like keyword ?based document retrieval. In this review paper, we present A secure multi keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operation like deletion and insertion of documents. Specifically, the vector space model and the widely-used 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 means illusory terms are added to the index vector for blinding search results. Due to the use of special tree-based index structure, the proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents flexibly.