Clustering Based K-anonymity Algorithm for Privacy Preservation

K-anonymity is an effective model for protecting privacy while publishing data, which can be implemented by different ways. Among them, local generalization are popular because of its low information loss. But such algorithms are generally computation expensive making it difficult to perform well in the case of large amount of data. In order to solve this problem, this paper proposes a clustering based K-anonymity algorithm and optimizes it with parallelization. The experimental result shows that the algorithm performs better in information loss and performance compared with the existing KACA and Incognito algorithms.

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