Paper 2019/1314

Towards Privacy-Preserving and Efficient Attribute-Based Multi-Keyword Search

Zhidan Li, Wenmin Li, Fei Gao, Wei Yin, Hua Zhang, Qiaoyan Wen, and Kaitai Liang

Abstract

Searchable encryption can provide secure search over encrypted cloud-based data without infringing data confidentiality and data searcher privacy. In this work, we focus on a secure search service providing fine-grained and expressive search functionality, which can be seen as a general extension of searchable encryption and called attribute-based multi-keyword search (ABMKS). In most of the existing ABMKS schemes, the ciphertext size of keyword index (encrypted index) grows linearly with the number of the keyword associated with a file, so that the computation and communication complexity of keyword index is limited to O(m) , where m is the number of the keyword. To address this shortage, we propose the first ABMKS scheme through utilizing keyword dictionary tree and the subset cover, in such a way that the ciphertext size of keyword index is not dependent on the number of underlying keyword in a file. In our design, the complexity of computation and the complexity of the keyword index are at most O ( 2· log (n/2) ) for the worst case, but O(1) for the best case, where n is the number of keyword in a keyword dictionary. We also present the security and the performance analysis to demonstrate that our scheme is both secure and efficient in practice.