Privacy-Preserving Smart Semantic Search Based on Conceptual Graphs over Encrypted Outsourced Data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

41 Scopus Citations
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  • Zhangjie Fu
  • Fengxiao Huang
  • Kui Ren
  • Jian Weng
  • Cong Wang

Related Research Unit(s)


Original languageEnglish
Article number7895156
Pages (from-to)1874-1884
Journal / PublicationIEEE Transactions on Information Forensics and Security
Issue number8
StatePublished - 1 Aug 2017


Searchable encryption is an important research area in cloud computing. However, most existing efficient and reliable ciphertext search schemes are based on keywords or shallow semantic parsing, which are not smart enough to meet with users' search intention. Therefore, in this paper, we propose a content-aware search scheme, which can make semantic search more smart. First, we introduce conceptual graphs (CGs) as a knowledge representation tool. Then, we present our two schemes (PRSCG and PRSCG-TF) based on CGs according to different scenarios. In order to conduct numerical calculation, we transfer original CGs into their linear form with some modification and map them to numerical vectors. Second, we employ the technology of multi-keyword ranked search over encrypted cloud data as the basis against two threat models and raise PRSCG and PRSCG-TF to resolve the problem of privacy-preserving smart semantic search based on CGs. Finally, we choose a real-world data set: CNN data set to test our scheme. We also analyze the privacy and efficiency of proposed schemes in detail. The experiment results show that our proposed schemes are efficient.

Research Area(s)

  • Cloud computing, Conceptual graphs, Searchable encryption, Smart semantic search

Citation Format(s)