Secure re-publication of dynamic big data

Kok Seng Wong, Myung Ho Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Dynamic data re-publication is now an emerging issue in data publishing due to the awareness of privacy disclosure in data sharing. Existing models such as k-anonymity and (-diversity only aim to provide data protection for single release. In practical, new data arrive continuously and up-to-date dataset should be released from time to time. The release of multiple anony-mized datasets (microdata) allows the attackers to learn extra knowledge by cross examines the releases within a targeted timeframe. In this paper, we study the data re-publication of dynamic big data based on the m-invariance model. In particular, we reconstruct the existing model to support re-publication for big data. We consider re-publication with insertion, deletion and update of the existing records. Counterfeit records will be used to maintain the update pattern of all the releases and to increase the false information in the knowledge learned by the attacker from the released microdata.

Original languageEnglish
Title of host publicationCyberspace Safety and Security - 5th International Symposium, CSS 2013, Proceedings
Number of pages10
Publication statusPublished - Dec 1 2013
Externally publishedYes
Event5th International Symposium on Cyberspace Safety and Security, CSS 2013 - Zhangjiajie, China
Duration: Nov 13 2013Nov 15 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8300 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Symposium on Cyberspace Safety and Security, CSS 2013


  • Big data privacy
  • Dynamic data re-publication
  • M-invariance
  • Privacy-preserving data publishing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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