LAUSR

Traditional term frequency‐inverse document frequency model‐based privacy‐preserving ranked search schemes rarely consider the latent semantic meanings of documents and keywords. It is a challenge to design efficient semantic‐aware ranked search (SRSE) schemes with privacy preservation. In this paper, two privacy‐preserving SRSE schemes are developed for the cloud environments. The first scheme is the accuracy‐first search scheme. In this scheme, the Latent Dirichlet Allocation topic model is adopted to generate the topic‐based semantic information‐embedded vectors for documents and queried keywords, which supports semantic‐aware relevance measurement. The bisecting k‐means clustering algorithm is used to build an accuracy‐first filtering tree index (AFF‐tree), and the AFF‐tree‐based search algorithm is proposed to achieve the accuracy‐first ranked search. The second scheme is the efficiency‐first search scheme. It performs a structure optimization on the AFF‐tree, and a newly efficiency‐first filtering tree index (EFF‐tree) is designed. By using the EFF‐tree, an anchor node‐based search algorithm is designed to achieve the efficiency‐first ranked search at the expense of a little decrease in search result precision. The secure inner product is used to perform privacy‐preserving semantic‐aware relevance measurement between documents and queried keywords in both schemes. To analyze the security of the proposed schemes, the game stimulation‐based proof is presented. Experimental results show the better performance of the proposed schemes in search time cost.