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A General Framework for Publishing Privacy Protected and Utility Preserved Graph

Authors Yuan, Mingxuan
Chen, Lei View this author's profile
Rao, Weixiong HKUST affiliated (currently or previously)
Mei, Hong
Issue Date 2012
Source IEEE International Conference on Data Mining , 2012, p. 1182-1187
Summary The privacy protection of graph data has become more and more important in recent years. Many works have been proposed to publish a privacy preserving graph. All these works prefer publishing a graph, which guarantees the protection of certain privacy with the smallest change to the original graph. However, there is no guarantee on how the utilities are preserved in the published graph. In this paper, we propose a general fine-grained adjusting framework to publish a privacy protected and utility preserved graph. With this framework, the data publisher can get a trade-off between the privacy and utility according to his customized preferences. We used the protection of a weighted graph as an example to demonstrate the implementation of this framework.
ISSN 1550-4786
ISBN 9780769549057
Language English
Format Conference paper
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