2016年
Bottom-Up Cell Suppression that Preserves the Missing-at-random Condition
Trust, Privacy and Security in Digital Business
- ,
- 巻
- 9830
- 号
- 開始ページ
- 65
- 終了ページ
- 78
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1007/978-3-319-44341-6_5
- 出版者・発行元
- SPRINGER INT PUBLISHING AG
This paper proposes a cell-suppression based k-anonymization method which keeps minimal the loss of utility. The proposed method uses the Kullback-Leibler (KL) divergence as a utility measure derived from the notions developed in the literature of incomplete data analysis, including the missing-at-random (MAR) condition. To be more specific, we plug the KL divergence into an bottom-up, greedy procedure for a local recoding k-anonymization as a cost function which is efficiently computed. We focus on classification datasets and experimental results exhibit that the proposed method yields a small degradation of classification performance when combined with naive Bayes classifiers.
- リンク情報
- ID情報
-
- DOI : 10.1007/978-3-319-44341-6_5
- ISSN : 0302-9743
- Web of Science ID : WOS:000389334200005