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Incremental Privacy-Preserving Association Rule Mining Using Negative Border

  • Duc H. Tran
  • Wee Keong Ng
  • Y. D. Wong
  • Vinh V. Thai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9650)

Abstract

Privacy preserving association rule mining can extract important rules from distributed data with limited privacy breaches. Protecting privacy in incremental maintenance for distributed association rule mining is necessary since data are frequently updated. In privacy preserving data mining, scanning all the distributed data is very costly. This paper proposes a new incremental protocol for privacy preserving association rule mining using negative border concept. The protocol scans old databases at most once, and therefore reducing the I/O time. We also conduct experiments to show the efficiency of our protocol over existing ones.

Keywords

Privacy preserving Secure protocol Association rule mining Negative border Incremental 

Notes

Acknowledgments

This research is supported by the Singapore Maritime Institute.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Duc H. Tran
    • 1
    • 2
  • Wee Keong Ng
    • 1
  • Y. D. Wong
    • 2
    • 3
  • Vinh V. Thai
    • 4
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Maritime InstituteNanyang Technological UniversitySingaporeSingapore
  3. 3.School of Civil and Environmental EngineeringNanyang Technological UniversitySingaporeSingapore
  4. 4.School of Business IT and LogisticsRMIT UniversityMelbourneAustralia

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