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Author, Editor

Author(s):

Bredereck, Robert
Nichterlein, Andr{\'e}
Niedermeier, Rolf
Philip, Geevarghese

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Not MPG Author(s):

Bredereck, Robert
Nichterlein, Andr{\'e}
Niedermeier, Rolf

Editor(s):

Murlak, Filip
Sankowski, Piotr

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dblp

Not MPII Editor(s):

Murlak, Filip
Sankowski, Piotr

BibTeX cite key*:

brederecknichterleinniedermeierphilip2011a

Title, Booktitle

Title*:

Pattern-Guided Data Anonymization and Clustering


patternClustering.pdf (447.62 KB)

Booktitle*:

Mathematical Foundations of Computer Science 2011 - 36th International Symposium, MFCS 2011, Warsaw, Poland, August 22-26, 2011. Proceedings

Event, URLs

URL of the conference:

http://mfcs.mimuw.edu.pl/

URL for downloading the paper:

http://www.springerlink.com/content/k3q036770up07x70/fulltext.pdf

Event Address*:

Warsaw, Poland

Language:

English

Event Date*
(no longer used):


Organization:


Event Start Date:

22 August 2011

Event End Date:

26 August 2011

Publisher

Name*:

Springer

URL:

http://www.springer.com

Address*:

Berlin

Type:


Vol, No, Year, pp.

Series:

Lecture Notes in Computer Science

Volume:

6907

Number:


Month:


Pages:

182-193

Year*:

2011

VG Wort Pages:


ISBN/ISSN:

978-3-642-22992-3

Sequence Number:


DOI:

10.1007/978-3-642-22993-0



Note, Abstract, ©


(LaTeX) Abstract:

A matrix~$M$ over a fixed alphabet is $k$-anonymous if every row
in~$M$ has at least~$k-1$ identical copies in~$M$. Making a matrix
$k$-anonymous by replacing a minimum number of entries with an additional
$\star$-symbol (called ``suppressing entries'') is known to be
NP-hard. This task arises in the context of privacy-preserving
publishing. We propose and analyze the computational complexity
of an enhanced anonymization model where the user of the
$k$-anonymized data may additionally ``guide'' the selection of
the candidate matrix entries to be suppressed. The basic idea is
to express this by means of ``pattern vectors'' which are part
of the input. This can also be interpreted as a sort of
clustering process. It is motivated by the observation that
the ``value'' of matrix entries may significantly differ, and
losing one (by suppression) may be more harmful than losing
the other, which again may very much depend on the intended use of the
anonymized data. We show that already very basic special cases
of our new model lead to NP-hard problems while others allow for
(fixed-parameter) tractability results.

URL for the Abstract:

http://www.springerlink.com/content/k3q036770up07x70/

Keywords:

Parameterized Algorithms, $k$-anonymous, suppressions

Copyright Message:

Copyright Springer Berlin 2011. This work is subject to copyright. All rights are reserved, whether the whole or part of the material is
concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965,
in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law.


Published in the Proceedings of MFCS 2011, Warsaw, Poland, August 22-26, 2011. Lecture Notes in Computer Science, Volume 6907. The original publication is available at www.springerlink.com : http://www.springerlink.com/content/k3q036770up07x70/


Download
Access Level:

Public

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Algorithms and Complexity Group

Audience:

experts only

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{brederecknichterleinniedermeierphilip2011a,
AUTHOR = {Bredereck, Robert and Nichterlein, Andr{\'e} and Niedermeier, Rolf and Philip, Geevarghese},
EDITOR = {Murlak, Filip and Sankowski, Piotr},
TITLE = {Pattern-Guided Data Anonymization and Clustering},
BOOKTITLE = {Mathematical Foundations of Computer Science 2011 - 36th International Symposium, MFCS 2011, Warsaw, Poland, August 22-26, 2011. Proceedings},
PUBLISHER = {Springer},
YEAR = {2011},
VOLUME = {6907},
PAGES = {182--193},
SERIES = {Lecture Notes in Computer Science},
ADDRESS = {Warsaw, Poland},
ISBN = {978-3-642-22992-3},
DOI = {10.1007/978-3-642-22993-0},
}


Entry last modified by Geevarghese Philip, 07/08/2014
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Created
04/22/2012 01:29:57 PM
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Editor
Geevarghese Philip
Geevarghese Philip


Edit Date
04/22/2012 03:25:42 PM
04/22/2012 01:29:57 PM


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