Faster optimal univariate microaggregation

Published: 23 Oct 2024, Last Modified: 23 Oct 2024Accepted by TMLREveryoneRevisionsBibTeXCC BY 4.0
Abstract: Microaggregation is a method to coarsen a dataset, by optimally clustering data points in groups of at least k points, thereby providing a $k$-anonymity type disclosure guarantee for each point in the dataset. Previous algorithms for univariate microaggregation had a $O(kn)$ time complexity. By rephrasing microaggregation as an instance of the concave least weight subsequence problem, in this work we provide improved algorithms that provide an optimal univariate microaggregation on sorted data in $O(n)$ time and space. We further show that our algorithms work not only for sum of squares cost functions, as typically considered, but seamlessly extend to many other cost functions used for univariate microaggregation tasks. In experiments we show that the presented algorithms lead to performance improvements on real hardware.
Submission Length: Long submission (more than 12 pages of main content)
Changes Since Last Submission: In this Submission we have incorporated the feedback from the action editor. We have added references to differential privacy in the conclusion, highlighting its use. Further we have added a reference to a paper that shows that k-anonymity combined with sampling may provide differential privacy guarantees. Lastly we have added an additional comparison with a method designed for multivariate case proposed by Domingo-Ferrer et. al. 2008.
Code: https://github.com/Feelx234/microagg1d
Assigned Action Editor: ~Antti_Koskela1
Submission Number: 2932
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