Extending l-diversity to generalize sensitive data
Data & Knowledge Engineering
A Knowledge Model Sharing Based Approach to Privacy-Preserving Data Mining
Transactions on Data Privacy
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The notions of l-diversity provides a strong privacy guarantee for generalization. However, existing l-diversity algorithms may force users to choose between publishing no data or scarifying privacy if the data have a skewed distribution of SA values. In this paper, we solve this problem by extending l-diversity in two ways. First, we allow the generalization of SA values and second, we use a simple function to constraint frequencies of SA values. The resulting (τ, l)-diversity is more flexible and elaborate. We present an efficient heuristic algorithm that uses a novel order of quasi-identifier values to achieve (τ, l)-diversity. We compare our algorithm with two state-of-the-art algorithms based on existing l-diversity measures. Our preliminary experimental results indicate that our algorithm cannot only effectively deal with data with skewed SA distributions but also result in better utility of anonymous data in general.