Privacy-preserving data publishing: A constraint-based clustering approach

Anthony K.H. Tung, Jiawei Han, Laks V.S. Lakshmanan, Raymond T. Ng

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Abstract Privacy-preserving data publishing has drawn much research interest re-cently. In this chapter, we address this topic from the viewpoint of constrained clustering, i.e., the problem of finding clusters that satisfy certain user-specified constraints. More specifically, we begin with the problem of clustering under aggregate constraints (without privacy considerations) and explain how traditional algorithms for the unconstrained problem (e.g., the c-means algorithm) break down in the presence of constraints. From there, we develop scalable algorithms that overcome this problem and finally illustrate how our algorithm can also be used for privacy-preserving data publishing.

Original languageEnglish (US)
Title of host publicationConstrained Clustering
Subtitle of host publicationAdvances in Algorithms, Theory, and Applications
PublisherCRC Press
Pages375-396
Number of pages22
ISBN (Electronic)9781584889977
ISBN (Print)9781584889960
StatePublished - Jan 1 2008

Fingerprint

Data privacy
Clustering
Privacy preserving

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Computer Science(all)
  • Economics, Econometrics and Finance(all)

Cite this

Tung, A. K. H., Han, J., Lakshmanan, L. V. S., & Ng, R. T. (2008). Privacy-preserving data publishing: A constraint-based clustering approach. In Constrained Clustering: Advances in Algorithms, Theory, and Applications (pp. 375-396). CRC Press.

Privacy-preserving data publishing : A constraint-based clustering approach. / Tung, Anthony K.H.; Han, Jiawei; Lakshmanan, Laks V.S.; Ng, Raymond T.

Constrained Clustering: Advances in Algorithms, Theory, and Applications. CRC Press, 2008. p. 375-396.

Research output: Chapter in Book/Report/Conference proceedingChapter

Tung, AKH, Han, J, Lakshmanan, LVS & Ng, RT 2008, Privacy-preserving data publishing: A constraint-based clustering approach. in Constrained Clustering: Advances in Algorithms, Theory, and Applications. CRC Press, pp. 375-396.
Tung AKH, Han J, Lakshmanan LVS, Ng RT. Privacy-preserving data publishing: A constraint-based clustering approach. In Constrained Clustering: Advances in Algorithms, Theory, and Applications. CRC Press. 2008. p. 375-396
Tung, Anthony K.H. ; Han, Jiawei ; Lakshmanan, Laks V.S. ; Ng, Raymond T. / Privacy-preserving data publishing : A constraint-based clustering approach. Constrained Clustering: Advances in Algorithms, Theory, and Applications. CRC Press, 2008. pp. 375-396
@inbook{84befac0d293444eae9b8ea45728610d,
title = "Privacy-preserving data publishing: A constraint-based clustering approach",
abstract = "Abstract Privacy-preserving data publishing has drawn much research interest re-cently. In this chapter, we address this topic from the viewpoint of constrained clustering, i.e., the problem of finding clusters that satisfy certain user-specified constraints. More specifically, we begin with the problem of clustering under aggregate constraints (without privacy considerations) and explain how traditional algorithms for the unconstrained problem (e.g., the c-means algorithm) break down in the presence of constraints. From there, we develop scalable algorithms that overcome this problem and finally illustrate how our algorithm can also be used for privacy-preserving data publishing.",
author = "Tung, {Anthony K.H.} and Jiawei Han and Lakshmanan, {Laks V.S.} and Ng, {Raymond T.}",
year = "2008",
month = "1",
day = "1",
language = "English (US)",
isbn = "9781584889960",
pages = "375--396",
booktitle = "Constrained Clustering",
publisher = "CRC Press",

}

TY - CHAP

T1 - Privacy-preserving data publishing

T2 - A constraint-based clustering approach

AU - Tung, Anthony K.H.

AU - Han, Jiawei

AU - Lakshmanan, Laks V.S.

AU - Ng, Raymond T.

PY - 2008/1/1

Y1 - 2008/1/1

N2 - Abstract Privacy-preserving data publishing has drawn much research interest re-cently. In this chapter, we address this topic from the viewpoint of constrained clustering, i.e., the problem of finding clusters that satisfy certain user-specified constraints. More specifically, we begin with the problem of clustering under aggregate constraints (without privacy considerations) and explain how traditional algorithms for the unconstrained problem (e.g., the c-means algorithm) break down in the presence of constraints. From there, we develop scalable algorithms that overcome this problem and finally illustrate how our algorithm can also be used for privacy-preserving data publishing.

AB - Abstract Privacy-preserving data publishing has drawn much research interest re-cently. In this chapter, we address this topic from the viewpoint of constrained clustering, i.e., the problem of finding clusters that satisfy certain user-specified constraints. More specifically, we begin with the problem of clustering under aggregate constraints (without privacy considerations) and explain how traditional algorithms for the unconstrained problem (e.g., the c-means algorithm) break down in the presence of constraints. From there, we develop scalable algorithms that overcome this problem and finally illustrate how our algorithm can also be used for privacy-preserving data publishing.

UR - http://www.scopus.com/inward/record.url?scp=84996807286&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84996807286&partnerID=8YFLogxK

M3 - Chapter

AN - SCOPUS:84996807286

SN - 9781584889960

SP - 375

EP - 396

BT - Constrained Clustering

PB - CRC Press

ER -