Journal of Combinatorial Theory Series B
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
LHS-Based Hybrid Microdata vs Rank Swapping and Microaggregation for Numeric Microdata Protection
Inference Control in Statistical Databases, From Theory to Practice
Disclosure Risk Assessment in Perturbative Microdata Protection
Inference Control in Statistical Databases, From Theory to Practice
Exact and approximate methods for data directed microaggregation in one or more dimensions
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Minimum Spanning Tree Partitioning Algorithm for Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
ICDT'05 Proceedings of the 10th international conference on Database Theory
A polynomial-time approximation to optimal multivariate microaggregation
Computers & Mathematics with Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICICS'07 Proceedings of the 9th international conference on Information and communications security
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
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Microaggregation is a special clustering problem where the goal is to cluster a set of points into groups of at least k points in such a way that groups are as homogeneous as possible. Microaggregation arises in connection with anonymization of statistical databases for privacy protection (k-anonymity), where points are assimilated to database records. A usual group homogeneity criterion is within-groups sum of squares minimization SSE. For multivariate points, optimal microaggregation, i.e. with minimum SSE, has been shown to be NP-hard. Recently, a polynomial-time O(k3)-approximation heuristic has been proposed (previous heuristics in the literature offered no approximation bounds). The special case k=2 (2-microaggregation) is interesting in privacy protection scenarios with neither internal intruders nor outliers, because information loss is lower: smaller groups imply smaller information loss. For 2-microaggregation the existing general approximation can only guarantee a 54-approximation. We give here a new polynomial-time heuristic whose SSE is at most twice the minimum SSE (2-approximation).