Advanced Research in Data Privacy

Volume 567 of the series Studies in Computational Intelligence pp 41-61


A Review of Attribute Disclosure Control

  • Stan MatwinAffiliated withFaculty of Computer Science, Dalhousie UniversityInstitute for Computer Science, Polish Academy of Sciences
  • , Jordi NinAffiliated withBarcelona Supercomputing Center (BSC), Universitat Politècnica de Catalunya (BarcelonaTech) Email author 
  • , Morvarid SehatkarAffiliated withSchool of Electrical Engineering and Computer Science, University of Ottawa
  • , Tomasz SzapiroAffiliated withDivision of Decision Analysis and Support, Warsaw School of Economics

* Final gross prices may vary according to local VAT.

Get Access


Attribute disclosure occurs when the adversary can infer some sensitive information about an individual without identifying individual’s record in the published data set. To address this issue several privacy models were proposed with the goal of increasing the uncertainty of the adversary in deriving sensitive information from published data. In this chapter, firstly we review the underlying scenario used in statistical disclosure control (SDC) and Privacy-Preserving Data Mining (PPDM). In this chapter, we describe the attribute disclosure underlying scenario, the different forms of background knowledge of the adversary the adversary may have and their potential privacy attacks. then, we review the approaches introduced in the literature to tackle attribute disclosure attacks.


Microaggregation k-anonymity p-sensitivity l-diversity Distributed dataset anonymization