Achieving Multidimensional K-Anonymity by a Greedy Approach
Sri Sai · 2011
Protecting privacy in microdata publishing is K-Anonymity, Here recoding “models” have been considered for achieving k anonymity[1,2]. We proposes a new multidimensional model, which gives high flexibility. Often this flexibility leads to higher-quality anonymizations, as measured both by generalpurpose metrics and more specific notions of query answerability. Like previous multidimensional models anonymization is NP-hard. However, we introduce a simple greedy approximation algorithm, It leads to more desirable anonymizations than single-dimensional models.