Semantic-aware multi-keyword ranked search scheme over encrypted cloud data

Abstract

Traditional searchable encryption schemes mostly adopt the TF-IDF (term frequency - inverse document frequency) model which ignores the semantic association between keywords and documents. It is a challenge to design an effective and secure semantic-aware search scheme. The topic model is based on “high-order co-occurrence”, i.e., how often words co-occur in different contexts. Hence, it can be utilized for modeling the latent semantics among texts. In this paper, we propose a novel privacy-persevering searchable encryption scheme based on the Latent Dirichlet Allocation (LDA) topic model. Documents are modeled by LDA, and the concept of topics is utilized to generate a document-topic relevance matrix and query topic vectors. The matrix is used as the index for the proposed scheme. The secure inner product operation is adopted to encrypt the index and query topic vectors, which provides accurate topic relevance score calculation between encrypted index and trapdoors. To improve the efficiency of our basic scheme, we adopt a special complete binary tree and use the “Greedy Depth First Search” algorithm. Our evaluation results demonstrate the effectiveness of our scheme.