Privacy preserving clustering with constraints
Computational Complexity
2018-02-19 v2
Abstract
The -center problem is a classical combinatorial optimization problem which asks to find centers such that the maximum distance of any input point in a set to its assigned center is minimized. The problem allows for elegant -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 clients will be assigned to it. We show how to combine privacy with several other constraints.
Cite
@article{arxiv.1802.02497,
title = {Privacy preserving clustering with constraints},
author = {Clemens Rösner and Melanie Schmidt},
journal= {arXiv preprint arXiv:1802.02497},
year = {2018}
}