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Received:January 09, 2008 Revised:August 28, 2008
Received:January 09, 2008 Revised:August 28, 2008
Abstract:In the research of privacy preserving data publishing, the present method always removes the individual identification attributes and then anonymizes the quasi-identifier attributes. This paper analyzes the situation of multiple records one individual and proposes the principle of identity-reserved anonymity. This method reserves more information while maintaining the individual privacy. The generalization and loss-join approaches are developed to meet this requirement. The algorithms are evaluated in an experimental scenario, reserving more information and demonstrating practical applicability of the approaches.
keywords: privacy preservation data publishing anonymity identity-reserved lossy join generalization
Foundation items:
Author Name | Affiliation |
TONG Yun-Hai | 机器感知与智能教育部重点实验室(北京大学),北京 100871 |
TAO You-Dong | |
TANG Shi-Wei | |
YANG Dong-Qing |
Reference text:
TONG Yun-Hai,TAO You-Dong,TANG Shi-Wei,YANG Dong-Qing.Identity-Reserved Anonymity in Privacy Preserving Data Publishing.Journal of Software,2010,21(4):771-781
TONG Yun-Hai,TAO You-Dong,TANG Shi-Wei,YANG Dong-Qing.Identity-Reserved Anonymity in Privacy Preserving Data Publishing.Journal of Software,2010,21(4):771-781