A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

Due to the growing recognition of cloud computing, increasingly facts proprietors are encouraged to outsource their statistics to cloud servers for notable convenience and decreased price in facts management. However, touchy facts must be encrypted before outsourcing for privacy requirements, which obsoletes facts utilization like keyword-based file retrieval. In this paper, we present a relaxed multi-keyword ranked search scheme over encrypted cloud records, which simultaneously support dynamic replace operations like deletion and insertion of documents. Specifically, the vector space model and the broadly-used TF IDF version are blended within the index creation and question era. We construct a unique tree-based index structure and suggest a “Greedy Depth-first Search� set of rules to offer efficient multi-key-word ranked seek. The comfy kNN algorithm is applied to encrypt the index and question vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and question vectors. In order to face up to statistical attacks, phantom phrases are delivered to the index vector for blinding search outcomes. Due to using our unique tree-primarily based index Structure, the proposed scheme can acquire sub-linear seek time and deal with the deletion and insertion of files flexibly. Extensive experiments are carried out to demonstrate the efficiency of the proposed scheme.