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- W2963916887 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 l clients will be assigned to it. We show how to combine privacy with several other constraints." @default.
- W2963916887 created "2019-07-30" @default.
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- W2963916887 date "2018-01-01" @default.
- W2963916887 modified "2023-09-27" @default.
- W2963916887 title "Privacy Preserving Clustering with Constraints" @default.
- W2963916887 doi "https://doi.org/10.4230/lipics.icalp.2018.96" @default.
- W2963916887 hasPublicationYear "2018" @default.
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