An Efficient Privacy-Preserving Ranked Keyword Search Method

Chi Chen, Xiaojie Zhu, Peisong Shen, Jiankun Hu, Song Guo, Zahir Tari, Albert Y. Zomaya

Research output: Journal article publicationJournal articleAcademic researchpeer-review

93 Citations (Scopus)

Abstract

Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.
Original languageEnglish
Article number7091954
Pages (from-to)951-963
Number of pages13
JournalIEEE Transactions on Parallel and Distributed Systems
Volume27
Issue number4
DOIs
Publication statusPublished - 1 Apr 2016
Externally publishedYes

Keywords

  • big data
  • ciphertext search
  • Cloud computing
  • hierarchical clustering
  • multi-keyword search
  • ranked search
  • security

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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