Privacy Characterization and Quantification in Data Publishing

M. H. Afifi, Kai Zhou, Jian Ren

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)


The increasing interest in collecting and publishing large amounts of individuals' data as public for purposes such as medical research, market analysis, and economical measures has created major privacy concerns about individual's sensitive information. To deal with these concerns, many Privacy-Preserving Data Publishing (PPDP) techniques have been proposed in literature. However, they lack a proper privacy characterization and measurement. In this paper, we first present a novel multi-variable privacy characterization and quantification model. Based on this model, we are able to analyze the prior and posterior adversarial belief about attribute values of individuals. We can also analyze the sensitivity of any identifier in privacy characterization. Then, we show that privacy should not be measured based on one metric. We demonstrate how this could result in privacy misjudgment. We propose two different metrics for quantification of privacy leakage, distribution leakage, and entropy leakage. Using these metrics, we analyzed some of the most well-known PPDP techniques such as κ -anonymity, l -diversity, and t -closeness. Based on our framework and the proposed metrics, we can determine that all the existing PPDP schemes have limitations in privacy characterization. Our proposed privacy characterization and measurement framework contributes to better understanding and evaluation of these techniques. Thus, this paper provides a foundation for design and analysis of PPDP schemes.

Original languageEnglish
Article number8276593
Pages (from-to)1756-1769
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number9
Publication statusPublished - 1 Sep 2018
Externally publishedYes


  • big data
  • data mining
  • Data privacy
  • data publishing
  • data security
  • privacy leakage
  • privacy quantification

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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