The increase in the amount of data stored in the cloud leads to the need for privacy-preserving multi-keyword search schemes in the cloud. However, most of the existing schemes usually adopt the TF-IDF vector space model, in which the vectors are high-dimensional and sparse. It results in substantial computation time and storage space. To address the issue, we propose an efficient dictionary partition-based multi-keyword ranked search scheme (DPMRS) over encrypted cloud data. First, a dictionary partition-based vector space model (DPVSM) is designed, which can compress vector dimensions and hence accelerate relevance score computation between documents and search keywords. Based on DPVSM, a dictionary partition-based keyword distribution inverted index (DPKD-index) is presented. By using the index, a baseline privacy-preserving ranked search scheme is proposed. To further improve the efficiency of search services, the search tree structure is adopted and a novel double tier search tree-based index (DSTree-index) is designed. By using the optimized index, an enhanced search scheme (DPMRS+) is proposed. The security analysis indicates that the proposed scheme can protect the privacy of search processing, and the experimental results show that the proposed scheme outperforms the existing works in terms of storage size, search precision, and search time cost.

As the number of open spatial datasets continues to grow, there is a corresponding increase in demand for the ability to efficiently identify spatial datasets that align with users’ specific requirements. It has become a significant issue, resulting in the need for a variety of spatial dataset search requirements, including the need for range-based spatial dataset search. In this paper, we propose an efficient range-based top-k spatial dataset search processing for spatial information retrieval based on the quadtree-based region-dataset inverted index (QRDI-index). A relevance measurement between a spatial dataset and a search range is presented first, which is used to rank candidate results. To support efficient search processing, the QRDI-index is designed, which combines the inverted index, quadtree, and spatial datasets. Using the index, we propose an efficient search processing algorithm that filters the minimum tree nodes in the QRDI-index, and the search space is narrowed to these nodes. Experimental results on three real-world spatial data repositories validate the accuracy and efficiency of the proposed search scheme.

With the significant increase in open spatial datasets, there is a growing need to search for datasets that meet users’ requirements for decision-making and machine learning. This has become a prominent issue, leading to various spatial dataset search requirements, including the need for spatial dataset range search. In this paper, we propose spatial dataset range search schemes, which is the first systematic study of spatial dataset range search processing according to the best of our knowledge. A baseline spatial dataset range search scheme is first proposed to process spatial dataset range searches. To improve the search efficiency, we proposed two optimized search schemes, the accuracy-first optimized search scheme and the efficiency-first optimized search scheme. In the former optimized scheme, the spatial dataset-MBR-based R-tree (SDMR-tree) is designed to filter candidate datasets without compromising search accuracy. In the latter optimized scheme, the dataset-grid inverted index (DGI-index) storing the spatial dataset grid distributions is designed and used to determine the search result approximately. The search efficiency is further improved but with a bit loss of accuracy. Comprehensive experiments on real-world data validate the accuracy and efficiency of the proposed search schemes.

Traditional searchable encryption schemes for clouds are generally based on TF-IDF vector space model, but they ignore the high-dimensional sparse characteristic of encrypted vectors. It will lead to substantial computational cost of inner product, and thus slows the search speed. In this paper, we propose a two-layer fast search index-based multi-keyword ranked search scheme (TFSRS) to address this problem. In the proposed TFSRS scheme, a keyword clustering-based equal-length dictionary partition (KCEDP) strategy is adopted to compress the document and search vectors, which benefits the process of inner product. Based on the strategy, a novel KCEDP-based vector space model (KCEDP-VSM) is proposed, and based on which, a two-layer fast search index (TFS-index) is presented. By using the TFS-index, secure inner product and symmetric encryption, the efficient multi-keyword ranked search scheme over encrypted cloud data is proposed. Experimental results show the better performance of the proposed schemes in search efficiency.