Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
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
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A Polynomial Algorithm for Optimal Univariate Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Minimum Spanning Tree Partitioning Algorithm for Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Efficient multivariate data-oriented microaggregation
The VLDB Journal — The International Journal on Very Large Data Bases
TFRP: An efficient microaggregation algorithm for statistical disclosure control
Journal of Systems and Software
Fast data anonymization with low information loss
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A polynomial-time approximation to optimal multivariate microaggregation
Computers & Mathematics with Applications
Importance partitioning in micro-aggregation
Computational Statistics & Data Analysis
A Genetic Approach to Multivariate Microaggregation for Database Privacy
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Approximation Bounds for Minimum Information Loss Microaggregation
IEEE Transactions on Knowledge and Data Engineering
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Microaggregation is a disclosure limitation method that provides security through k-anonymity by modifying data before release but does not allow suppression of data. We define the microaggregation problem with suppression (MPS) to accommodate data suppression, and present a polynomial-time algorithm, based on dynamic programming, for optimal univariate microaggregation with suppression. Experimental results demonstrate the practical benefits of suppressing a few carefully selected data points during microaggregation using our method.