License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
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DOI: 10.4230/LIPIcs.ICALP.2018.96
URN: urn:nbn:de:0030-drops-91008
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Rösner, Clemens ; Schmidt, Melanie

Privacy Preserving Clustering with Constraints

LIPIcs-ICALP-2018-96.pdf (0.4 MB)


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.

BibTeX - Entry

  author =	{Clemens R{\"o}sner and Melanie Schmidt},
  title =	{{Privacy Preserving Clustering with Constraints}},
  booktitle =	{45th International Colloquium on Automata, Languages, and  Programming (ICALP 2018)},
  pages =	{96:1--96:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-076-7},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{107},
  editor =	{Ioannis Chatzigiannakis and Christos Kaklamanis and D{\'a}niel Marx and Donald Sannella},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-91008},
  doi =		{10.4230/LIPIcs.ICALP.2018.96},
  annote =	{Keywords: Clustering, k-center, Constraints, Privacy, Lower Bounds, Fairness}

Keywords: Clustering, k-center, Constraints, Privacy, Lower Bounds, Fairness
Collection: 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018)
Issue Date: 2018
Date of publication: 04.07.2018

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