Avoiding Attribute Disclosure with the (Extended) p-Sensitive k-Anonymity Model
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.
KeywordsCategorical Attribute Data Owner Cost Measure Sensitive Attribute Privacy Enforcement
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