EDP-CVSM model-based multi-keyword ranked search scheme over encrypted cloud data

Traditional searchable encryption schemes for clouds are generally based on the term frequency-inverse document frequency (TF-IDF) vector space model, but they ignore the high-dimensional sparse characteristic of encrypted vectors. It could lead to substantial computational cost of the inner product. If the dimensionality and sparsity of encrypted vectors can be reduced or compressed, the search processing will be accelerated. To improve the search efficiency, we propose an encrypted two-layer balance binary tree index-based multi- keyword ranked search scheme (ETMRS) to address this problem in this paper. An equal-length dictionary partition-based compressed vector space model (EDP-CVSM) is presented, which introduces the dictionary partition strategy. It effectively compresses the document and search vectors, which benefits the efficiency of relevance score computation in search processing. In addition, to further improves the search efficiency, a two-layer balance binary tree index (TBBT-index) is proposed, which adopts secure inner product and symmetric encryption to preserve the privacy. The index is able to filter out the sub-dictionaries having no search keywords in the upper layer and identify the result documents in the lower layer, which speeds up the search processing. Experimental results show a good performance of the proposed scheme in file coverage rate, search precision, rank privacy, search efficiency and space consumption.