arXiv · 1802.02497
Privacy preserving clustering with constraints
Abstract
The $k$-center problem is a classical combinatorial optimization problem which asks to find $k$ centers such that the maximum distance of any input point in a set $P$ to its assigned center is minimized. The problem allows for elegant $2$-approximations. However, the situation becomes significantly more difficult when constraints are added to the problem. We raise the question whether general methods can be derived to turn an approximation algorithm for a clustering problem with some constraints into an approximation algorithm that respects one constraint more. Our constraint of choice is privacy: Here, we are asked to only open a center when at least $\ell$ clients will be assigned to it. We show how to combine privacy with several other constraints.
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Clemens Rösner, Melanie Schmidt. 2018-02-07. Privacy preserving clustering with constraints. https://arxiv.org/abs/1802.02497
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