Privacy-Preserving and Dynamic Multi-Attribute Conjunctive Keyword Search Over Encrypted Cloud Data

Abstract

With the increasing popularity of cloud computing, a growing data owners are motivated to outsource their huge data to cloud servers in order to facilitate access and save data management cost. To protect user privacy and data security, sensitive data should be encrypted before outsourced to the cloud server, which obsoletes data utilization like efficient search over encrypted data. In this paper, we present a privacy-preserving conjunctive keyword search scheme over encrypted cloud data, which simultaneously supports dynamic update operations. Specifically, we construct an index structure based on multi-attribute tree (MAT) and present an efficient search algorithm over the index tree, named as the searchMAT algorithm. We propose a multi-attribute conjunctive keyword search scheme based on MAT, named as the MCKS-MAT scheme, which can achieve equality conjunction, subset conjunction and range conjunction, as well as satisfy privacy requirements under the known background attack model. In addition, this paper is accompanied by an adequate of experiments for evaluating the effectiveness of the proposed scheme. Experiments demonstrate that, compared to the linear search, the proposed scheme needs the slightly higher preprocessing cost on account of constructing the tree-based index, however, it achieves lower computational overhead in initialization, trapdoor generation and queries.