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Journal of Intelligent Information Systems
Erschienen in: Journal of Intelligent Information Systems 2/2009

01.10.2009

(α, k)-anonymous data publishing

verfasst von: Raymond Wong, Jiuyong Li, Ada Fu, Ke Wang

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 2/2009

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Abstract

Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first present an optimal global-recoding method for the (α, k)-anonymity problem. Next we propose two scalable local-recoding algorithms which are both more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general cases.
Vorheriger Artikel The Multi-Tree Cubing algorithm for computing iceberg cubes

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Fußnoten
1
We use a simplified postcode scheme in this paper. There are four single digits, representing states, regions, cities and suburbs. Postcode 4350 indicates state-region-city-suburb.
 
2
After sorting, a set of contiguous tuples forms an equivalence class.
 
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Metadaten
Titel
(α, k)-anonymous data publishing
verfasst von
Raymond Wong
Jiuyong Li
Ada Fu
Ke Wang
Publikationsdatum
01.10.2009
Verlag
Springer US
Erschienen in
Journal of Intelligent Information Systems / Ausgabe 2/2009
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI
https://doi.org/10.1007/s10844-008-0075-2

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