An Efficient and Privacy-Preserving Semantic Multi-Keyword Ranked Search over Encrypted Cloud Data

As so much advantage of cloud computing, more and more data owners centralize their sensitive data into the cloud. With a mass of data files stored in the cloud server, it is important to provide keyword based search service to data user. However, in order to protect the data privacy, sensitive data is usually encrypted before outsourced to the cloud server, which makes the search technologies on plaintext unusable. In this paper, we propose a semantic multi-keyword ranked search scheme over the encrypted cloud data, which simultaneously meets a set of strict privacy requirements. Firstly, we utilize the "Latent Semantic Analysis" to reveal relationship between terms and documents. The latent semantic analysis takes advantage of implicit higher-order structure in the association of terms with documents ("semantic structure") and adopts a reduced-dimension vector space to represent words and documents. Thus, the relationship between terms is automatically captured. Secondly, our scheme employ secure "k-nearest neighbor (k-NN)" to achieve secure search functionality. The proposed scheme could return not only the exact matching files, but also the files including the terms latent semantically associated to the query keyword. Finally, the experimental result demonstrates that our method is better than the original MRSE scheme.