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Approximation Bounds for Minimum Information Loss Microaggregation
November 2009 (vol. 21 no. 11)
pp. 1643-1647
Michael Laszlo, Nova Southeastern University, Fort Lauderdale
Sumitra Mukherjee, Nova Southeastern University, Fort Lauderdale
The NP-hard microaggregation problem seeks a partition of data points into groups of minimum specified size k, so as to minimize the sum of the squared euclidean distances of every point to its group's centroid. One recent heuristic provides an {\rm O}(k^3) guarantee for this objective function and an {\rm O}(k^2) guarantee for a version of the problem that seeks to minimize the sum of the distances of the points to its group's centroid. This paper establishes approximation bounds for another microaggregation heuristic, providing better approximation guarantees of {\rm O}(k^2) for the squared distance measure and {\rm O}(k) for the distance measure.

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Index Terms:
Data security, disclosure control, microdata protection, microaggregation, k-anonymity, approximation algorithms, graph partitioning, information loss.
Citation:
Michael Laszlo, Sumitra Mukherjee, "Approximation Bounds for Minimum Information Loss Microaggregation," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 11, pp. 1643-1647, Nov. 2009, doi:10.1109/TKDE.2009.78
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