Achieving Efficient and Privacy-Preserving Multi-Keyword Conjunctive Query Over Cloud

Abstract

With the explosive growth of data, it has become increasingly popular to deploy the powerful cloud to manage data. Meanwhile, as the cloud is not always fully trusted, personal and sensitive data have to be encrypted before being outsourced to the cloud. Naturally, this brings a serious challenge for the cloud to provide secure and efficient query services over huge volumes of data. Although existing works have proposed some solutions to solve the above challenge, most of them just focus on the single keyword query and cannot directly support multi-keyword query. Even though some works have discussed solutions for the multi-keyword query, they cannot well balance the efficiency and privacy. Therefore, in this paper, we propose a novel multi-keyword conjunctive query scheme over cloud, which can achieve high query efficiency with small privacy leakage. In specific, we first design a tree-based index to support the multi-keyword conjunctive query and employ Boneh-Goh-Nissim (BGN) homomorphic encryption technique to protect its privacy. Then, based on the tree-based index, we propose a wildcard search algorithm to improve its query efficiency. Finally, the detailed security analysis shows that the proposed scheme is really privacy-preserving, and extensive simulation results also demonstrate its efficient.

Keywords

• Cloud computing
• conjunctive query
• homomorphic encryption
• multi-keyword query