CPP: Towards comprehensive privacy preserving for query processing in information networks

Chaobin Liu, Shuigeng Zhou, Haibo Hu, Yuzhe Tang, Jihong Guan, Yao Ma

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


This paper addresses the privacy issue for query processing in information networks, where users search and aggregate information from many data sources. To cater for the privacy requirements of all parties, we propose a comprehensive privacy preserving framework (CPP in short) for preserving data privacy, query privacy and storage privacy simultaneously, and implement this framework for key-value query processing. We first develop a baseline scheme that adopts commutative encryption with full indistinguishability guarantee. To speedup query processing, we then propose a tradeoff between security and efficiency, which leads to a scheme that significantly reduces the use of commutative encryption with a little and bounded security cost. Finally, we validate the proposed framework and the implementation schemes by both theoretical analysis and experimental results.

Original languageEnglish (US)
Pages (from-to)296-311
Number of pages16
JournalInformation sciences
StatePublished - Oct 2018


  • Information networks
  • Privacy preserving
  • Query processing

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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