Privacy preserving data publishing with multiple sensitive attributes based on overlapped slicing

Research output: Contribution to journalArticle

Abstract

Investigation into privacy preserving data publishing with multiple sensitive attributes is performed to reduce probability of adversaries to guess the sensitive values. Masking the sensitive values is usually performed by anonymizing data by using generalization and suppression techniques. A successful anonymization technique should reduce information loss due to the generalization and suppression. This research attempts to solve both problems in microdata with multiple sensitive attributes. We propose a novel overlapped slicing method for privacy preserving data publishing with multiple sensitive attributes. We used discernibility metrics to measure information loss. The experiment result shows that our method obtained a lower discernibility value than other methods.

Original languageEnglish
Article number362
JournalInformation (Switzerland)
Volume10
Issue number12
DOIs
Publication statusPublished - 1 Dec 2019

Keywords

  • Discernibility
  • Multiple sensitive attributes
  • Overlapped slicing
  • Privacy preserving data publishing

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