A privacy reinforcement approach against de-identified dataset

Ci Wei Lan*, Yi Hui Chen, Tyrone Grandison, Angus F.M. Huang, Jen Yao Chung, Li Feng Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Protection of individual privacy has been a key issue for the corresponding data dissemination. Nowadays powerful search utilities increase the re-identification risk by easier information collection as well as validation than before. Despite there usually performs certain de-identified process, attackers may recognize someone from released dataset with which attacker-owned information is matched. In this paper, we propose an approach to mitigate the identity disclosure problem by generating plurals in a given dataset. The approach leverages decision tree to help selection of quasi-identifier and several masking techniques can be employed for privacy reinforcement. In addition to different privacy metrics applicability, the approach can achieve better trade-off between data integrity and privacy protection through flexible data masking.

Original languageEnglish
Title of host publicationProceedings - 2011 8th IEEE International Conference on e-Business Engineering, ICEBE 2011
Pages370-375
Number of pages6
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 8th IEEE International Conference on e-Business Engineering, ICEBE 2011 - Beijing, China
Duration: 19 10 201121 10 2011

Publication series

NameProceedings - 2011 8th IEEE International Conference on e-Business Engineering, ICEBE 2011

Conference

Conference2011 8th IEEE International Conference on e-Business Engineering, ICEBE 2011
Country/TerritoryChina
CityBeijing
Period19/10/1121/10/11

Keywords

  • Privacy
  • data mask
  • microdata protection
  • quasi-identifier

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