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Microaggregation is an important technique to the k-anonymized datasets. However, most existing microaggregation algorithms to achieving k-anonymity have some defects on distance measurement for categorical and mixed data. In this paper, we introduce a categorical data semantic hierarchy to their distance measurement to improve clustering quality. The paper also investigates mixed distance for mixed...
K-anonymization implemented by microaggregation is a special clustering problem with minimum cardinality constraint, which has been proved to be an NP-hard combinatorial optimization problem. The existing heuristic microaggregation algorithms search the solutions in a limited solution space, so can only get local optimal solutions. The paper proposes an ICSMA (immune clonal selection microaggregation...
General (alpha,k)-anonymity model is an effective approach to protecting individual privacy before microdata are released. But it has some defects on privacy preservation and data distortion when the distribution of sensitive values is not well-proportioned. To solve the problem, a complete (alpha,k)-anonymity model is proposed which can implement sensitive values' individuation preservation by setting...
V-MDAV algorithm is a high efficient multivariate microaggregation algorithm and the anonymity table generated by the algorithm has high data quality. But it does not consider the sensitive attribute diversity, so the anonymity table generated by the algorithm cannot resist homogeneity attack and background knowledge attack. To solve the problem, the paper proposes an improved V-MDAV algorithm, which...
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