Practical Attribute-Based Conjunctive Keyword Search Scheme. (9th December 2019)

• Record Type: Journal Article
• Authors: Chen, Yang; Li, Wenmin; Gao, Fei; Liang, Kaitai; Zhang, Hua; Wen, Qiaoyan
• Is Part Of: Computer journal. Volume 63:Number 8(2020)
• Page Start: 1203
• Page End: 1215
• Publication Date: 2019-12-09
• Subjects: attribute-based keyword search -- conjunctive keyword search -- outside keyword guessing attack -- match privacy protection -- cloud storage; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxz140
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: To date cloud computing may provide considerable storage and computational power for cloud-based applications to support cryptographic operations. Due to this benefit, attribute-based keyword search (ABKS) is able to be implemented in cloud context in order to protect the search privacy of data owner/user. ABKS is a cryptographic primitive that can provide secure search services for users but also realize fine-grained access control over data. However, there have been two potential problems that prevent the scalability of ABKS applications. First of all, most of the existing ABKS schemes suffer from the outside keyword guessing attack (KGA). Second, match privacy should be considered while supporting multi-keyword search. In this paper, we design an efficient method to combine the keyword search process in ABKS with inner product encryption and deploy several proposed techniques to ensure the flexibility of retrieval mode, the security and efficiency of our scheme. We later put forward an attribute-based conjunctive keyword search scheme against outside KGA to solve the aforementioned problems. We provide security notions for two types of adversaries and our construction is proved secure against chosen keyword attack and outside KGA. Finally, all-side simulation with real-world data set is implemented for the proposed scheme, and the results of the simulation show that our scheme achieves stronger security without yielding significant cost of storage and computation.