An Efficient Multi-keyword Text Search Over Outsourced Encrypted Cloud Data with Ranked Results

Abstract

Cloud computing offers efficient deployment options that motivate large enterprises to outsource the data to the cloud. However, outsourcing sensitive information may compromise the privacy of the data. To enable keyword-based search over encrypted data, we proposed a multi-keyword search scheme on a tree-based encrypted index data structure to retrieve information from encrypted cloud data. In this model, the document collection is clustered using a hierarchical k-means method. A vector space model was used to create an encrypted index and query vectors, and a depth-first search algorithm is proposed for efficient search mechanism. The results were ranked based on relevance score between the encrypted index and query vectors. Rigorous experiments show the performance and efficiency of the proposed methods.