Abstract
Optimally micro-aggregating a multivariate data set is known to be NP-hard, thus, heuristic approaches are used to cope with this privacy preserving problem. Unfortunately, algorithms in the literature are computationally costly, and this prevents using them on large data sets.
We propose a partitioning algorithm to micro-aggregate uniform very large data sets with cost O(n). We provide the mathematical foundations proving the efficiency of our algorithm and we show that the error associated to micro-aggregation is bounded and decreases when the number of micro-aggregated records grows. The experimental results confirm the prediction of the mathematical analysis. In addition, we provide a comparison between our proposal and MDAV, a well-known micro-aggregation algorithm with cost O(n 2).
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Solanas, A., Di Pietro, R. (2008). A Linear-Time Multivariate Micro-aggregation for Privacy Protection in Uniform Very Large Data Sets. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2008. Lecture Notes in Computer Science(), vol 5285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88269-5_19
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DOI: https://doi.org/10.1007/978-3-540-88269-5_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88268-8
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