Abstract
We study the problem of r-anonymized clustering and give a k-means type extension. The problem is partition a set of objects into k different groups by minimizing the total cost between objects and cluster centers subject to a constraint that each cluster contains at least r objects. Previous work has reported an approach when the cluster centers are constrained to be a real member of the objects. In this paper, we release the constraint and allow a center to be the mean of the objects in its group, similar to the settings of the classical k-means clustering model. To address the inherent computational difficulty, we exploit linear program relaxation to find high quality solutions in an efficient manner. We conduct a series of experiments and confirm the effectiveness of the method as expected.
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Li, W. (2014). Privacy Preserving Clustering: A k-Means Type Extension. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_39
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DOI: https://doi.org/10.1007/978-3-319-12640-1_39
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12639-5
Online ISBN: 978-3-319-12640-1
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