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Achieving k-anonymity by clustering in attribute hierarchical structures

Publication ,  Conference
Jiuyong, L; Wong, RCW; Fu, AWC; Jian, P
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2006

Individual privacy will be at risk if a published data set is not properly de-identified, k-anonymity is a major technique to de-identify a data set. A more general view of k-anonymity is clustering with a constraint of the minimum number of objects in every cluster. Most existing approaches to achieving k-anonymity by clustering are for numerical (or ordinal) attributes. In this paper, we study achieving fc-anonymity by clustering in attribute hierarchical structures. We define generalisation distances between tuples to characterise distortions by generalisations and discuss the properties of the distances. We conclude that the generalisation distance is a metric distance. We propose an efficient clustering-based algorithm for k-anonymisation. We experimentally show that the proposed method is more scalable and causes significantly less distortions than an optimal global recoding k-anonymity method. © Springer-Verlag Berlin Heidelberg 2006.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783540377368

Publication Date

January 1, 2006

Volume

4081 LNCS

Start / End Page

405 / 416

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Jiuyong, L., Wong, R. C. W., Fu, A. W. C., & Jian, P. (2006). Achieving k-anonymity by clustering in attribute hierarchical structures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4081 LNCS, pp. 405–416).
Jiuyong, L., R. C. W. Wong, A. W. C. Fu, and P. Jian. “Achieving k-anonymity by clustering in attribute hierarchical structures.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4081 LNCS:405–16, 2006.
Jiuyong L, Wong RCW, Fu AWC, Jian P. Achieving k-anonymity by clustering in attribute hierarchical structures. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006. p. 405–16.
Jiuyong, L., et al. “Achieving k-anonymity by clustering in attribute hierarchical structures.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4081 LNCS, 2006, pp. 405–16.
Jiuyong L, Wong RCW, Fu AWC, Jian P. Achieving k-anonymity by clustering in attribute hierarchical structures. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006. p. 405–416.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783540377368

Publication Date

January 1, 2006

Volume

4081 LNCS

Start / End Page

405 / 416

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences