The searchable encryption scheme can perform keywords search operation directly over encrypted data without decryption, which is crucial to cloud storage, and has attracted a lot of attention in these years. However, it is still an open problem to develop an efficient public key encryption scheme supporting conjunctive and a disjunctive keyword search simultaneously. To achieve this goal, we introduce a keyword conversion method that can transform the query and index keywords into a vector space model. Through applying a vector space model to a predicate encryption scheme supporting inner product, we propose a novel public key encryption scheme with conjunctive and disjunctive keyword search. The experiment result demonstrates that our scheme is more efficient in both time and space as well as more suitable for the mobile cloud compared with the state-of-art schemes.

Searchable public key encryption scheme is a key technique for protecting data confidentiality in today’s cloud environment. Specifically, public key encryption with conjunctive and disjunctive keyword search (PECDK) can provide flexible search options without sacrificing keywords security and thus attracts a lot of attention nowadays. However, the most effective PECDK scheme is based on the inner product encryption (IPE), which needs more time and space cost. In this paper, by utilizing the bilinear pairing with a prime order group, we propose an efficient PECDK scheme needing less time and storage consumption. The proposed scheme is proven to be secure under a rigorous security definition. The theoretical analysis and experimental results demonstrate that our proposed scheme can significantly improve the time and space efficiency over the state-of-the-art scheme.

Searchable public key encryption (SPE) that supports multi-keywords search, allows data users to retrieve encrypted files of interest efficiently, and thus it has been intensively studied during recent years. However, most existing SPE solutions focus on the exact keyword matching, which fails to capture the semantic information of documents. In this paper, we develop a novel SPE scheme supporting semantic multi-keywords search over the encrypted data. Our solution is mainly built on two techniques: one is a shallow neural network model called ''word2vec'' for capturing the semantic keywords from documents; the other is a keywords conversion method which can convert keywords into a set of vectors. We then utilize an efficient inner product encryption scheme to encrypt these converted vectors and develop the target SPE scheme, which is proven to be secure against chosen keywords attacks. Moreover, we also present both theoretical and experimental analysis to verify the efficiency and accuracy of this scheme. The experiments over a real-world dataset demonstrate that our scheme can obtain a practical performance in terms of time and space complexities. To the best of our knowledge, it is the first time to construct semantic keywords search scheme over encrypted data in the public key setting.

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