Abstract
Microaggregation is a well-known family of statistical disclosure control methods, that can also be used to achieve the k-anonymity privacy model and some of its extensions. Microaggregation can be viewed as a clustering problem where clusters must include at least k elements. In this paper, we present a new microaggregation heuristic based on Lloyd’s clustering algorithm that causes much less information loss than the other microaggregation heuristics in the literature. Our empirical work consistently observes this superior performance for all minimum cluster sizes k and data sets tried.
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The following funding sources are gratefully acknowledged: European Commission (project H2020-700540 “CANVAS”), Government of Catalonia (ICREA Acadèmia Prize to J. Domingo-Ferrer and grant 2017 SGR 705) and Spanish Government (project RTI2018-095094-B-C21 “Consent”). The views in this paper are the authors’ own and do not necessarily reflect the views of UNESCO or any of the funders.
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Soria-Comas, J., Domingo-Ferrer, J., Mulero, R. (2019). Efficient Near-Optimal Variable-Size Microaggregation. In: Torra, V., Narukawa, Y., Pasi, G., Viviani, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2019. Lecture Notes in Computer Science(), vol 11676. Springer, Cham. https://doi.org/10.1007/978-3-030-26773-5_29
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