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Data Mining pp 353-373 | Cite as

Avoiding Attribute Disclosure with the (Extended) p-Sensitive k-Anonymity Model

  • Traian Marius Truta
  • Alina Campan
Chapter
Part of the Annals of Information Systems book series (AOIS, volume 8)

Abstract

Existing privacy regulations together with large amounts of available data created a huge interest in data privacy research. A main research direction is built around the k-anonymity property. Several shortcomings of the k-anonymity model were addressed by new privacy models such as p-sensitive k-anonymity, l-diversity, (α,k)-anonymity, t-closeness. In this chapter we describe two algorithms (GreedyPKClustering and EnhancedPKClustering) for generating (extended) p-sensitive k-anonymous microdata. In our experiments, we compare the quality of generated microdata obtained with the mentioned algorithms and with another existing anonymization algorithm (Incognito). Also, we present two new branches of p-sensitive k-anonymity, the constrained p-sensitive k-anonymity model and the p-sensitive k-anonymity model for social networks.

Keywords

Categorical Attribute Data Owner Cost Measure Sensitive Attribute Privacy Enforcement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Computer ScienceNorthern Kentucky UniversityHighland HeightsUSA

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