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Privacy Preserving Clustering with Constraints

Authors Clemens Rösner, Melanie Schmidt

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Clemens Rösner
  • Department of Theoretical Computer Science, University of Bonn, Germany
Melanie Schmidt
  • Department of Theoretical Computer Science, University of Bonn, Germany

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Clemens Rösner and Melanie Schmidt. Privacy Preserving Clustering with Constraints. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 96:1-96:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.ICALP.2018.96

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.

Subject Classification

ACM Subject Classification
  • Theory of computation → Facility location and clustering
Keywords
  • Clustering
  • k-center
  • Constraints
  • Privacy
  • Lower Bounds
  • Fairness

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