Abstract
K-anonymisation is a technique for protecting privacy contained within a dataset. Many k-anonymisation algorithms have been proposed, and one class of such algorithms are clustering-based. These algorithms can offer high quality solutions, but are rather inefficient to execute. In this paper, we propose a method that partitions a dataset into groups first and then clusters the data within each group for k-anonymisation. Our experiments show that combining partitioning with clustering can improve the performance of clustering-based k-anonymisation algorithms significantly while maintaining the quality of anonymisations they produce.
Keywords
- Privacy Protection
- Optimal Grouping
- Credit Card Number
- Small Subspace
- Shopping Preference
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Loukides, G., Shao, J. (2007). Speeding Up Clustering-Based k-Anonymisation Algorithms with Pre-partitioning. In: Cooper, R., Kennedy, J. (eds) Data Management. Data, Data Everywhere. BNCOD 2007. Lecture Notes in Computer Science, vol 4587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73390-4_23
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DOI: https://doi.org/10.1007/978-3-540-73390-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73389-8
Online ISBN: 978-3-540-73390-4
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