ADKAM: A-Diversity K-Anonymity Model via Microaggregation

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• 作者：Liang Cheng (15)
  Shaoyin Cheng (15)
  Fan Jiang (15)
  
  15. School of Computer Science and Technology ; University of Science and Technology of China ; Hefei ; 230037 ; China
  
• 关键词：Data publishing ; k ; anonymity ; privacy preservation ; microaggregation
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9065
• 期：1
• 页码：533-547
• 全文大小：257 KB
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• 作者单位：Information Security Practice and Experience
• 丛书名：978-3-319-17532-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

A great challenge in privacy preservation is to trade off two important issues: data utility and privacy preservation, in publication of dataset which usually contains sensitive information. Anonymization is a well-represent approach to achieve this, and there exist several anonymity models. Most of those models mainly focuses on protecting privacy exerting identical protection for the whole table with pre-defined parameters. As a result, it could not meet the diverse requirements of protection degrees varied with different sensitive values.Motivated by this, this paper firstly introduces an a-diversity k-anonymity model (ADKAM) to satisfy the diversity deassociation for sensitive values, ant then designs a framework based on an improved microaggregation algorithm, as an alternative to generalization/ suppression to achieve anonymization. By using this framework, we improve the data utility and disclosure risk of privacy disclosure. We conduct several experiments to validate our schemes.