Anonymization by local recoding in data with attribute hierarchical taxonomies
Publication in refereed journal


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摘要Individual privacy will be at risk if a published data set is not properly deidentified. k-Anonymity is a major technique to deidentify a data set. Among a number of k-anonymization schemes, local recoding methods are promising for minimizing the distortion of a k-anonymity view. This paper addresses two major issues in local recoding k-anonymization in attribute hierarchical taxonomies. First, we define a proper distance metric to achieve local recoding generalization with small distortion. Second, we propose a means to control the inconsistency of attribute domains in a generalized view by local recoding. We show experimentally that our proposed local recoding method based on the proposed distance metric produces higher quality k-anonymity tables in three quality measures than a global recoding anonymization method, Incognito, and a multidimensional recoding anonymization method, Multi. The proposed inconsistency handling method is able to balance distortion and consistency of a generalized view.
著者Li JY, Wong RCW, Fu AWC, Pei J
期刊名稱IEEE Transactions on Knowledge and Data Engineering
出版年份2008
月份9
日期1
卷號20
期次9
出版社IEEE COMPUTER SOC
頁次1181 - 1194
國際標準期刊號1041-4347
電子國際標準期刊號1558-2191
語言英式英語
關鍵詞generalization distance; inconsistency; k-anonymization; local recoding
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Computer Science, Information Systems; COMPUTER SCIENCE, INFORMATION SYSTEMS; Engineering; Engineering, Electrical & Electronic; ENGINEERING, ELECTRICAL & ELECTRONIC

上次更新時間 2021-20-01 於 01:19