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International Conference on Information, Electronic and Computer Science (ICIECS 2010 E-BOOK)

Zibo,China,11.26-11.28,2010

ISBN: 978-1-935068-42-6 Scientific Research Publishing, USA

E-Book 2224pp Pub. Date: November 2010

Category: Computer Science & Communications

Price: $360

Title: Anonymizing Tabular Data Using Bipartite Graph
Source: International Conference on Information, Electronic and Computer Science (ICIECS 2010 E-BOOK) (pp 167-170)
Author(s): Lihui Lan, Computer Science School of Jilin Normal University，SiPing，China
Yinghui Sun, Computer Science School of Jilin Normal University，SiPing，China
Hao Li, Computer Science School of Jilin Normal University，SiPing，China
Kun Hou, Computer Science School of Jilin Normal University，SiPing，China
Abstract: Most of the data privacy research has been focused on more traditional data models such as microdata. But private data often comes in the form of associations between entities, such as customers and products bought from a pharmacy. However, the focus has mostly been on tabular data, rather than associations. We give an approach for anonymizing associations which can be represented as bipartite graphs. We present a greedy maximum match group algorithm for anonymizing bipartite graph. These groups preserve the underlying graph structure, and instead anonymize the mapping from entities to nodes of the graph. We perform experiments on bipartite graph data to study the utility and information loss measure.
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