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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tiancheng Li Ninghui Li |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Comput. Sci., Purdue Univ., West Lafayette, IN (Tiancheng Li; Ninghui Li) |
| Abstract | In recent years, a major thread of research on k-anonymity has focused on developing more flexible generalization schemes that produce higher-quality datasets. In this paper we introduce three new generalization schemes that improve on existing schemes, as well as algorithms enumerating valid generalizations in these schemes. We also introduce a taxonomy for generalization schemes and a new cost metric for measuring information loss. We present a bottom-up search strategy for finding optimal anonymizations. This strategy works particularly well when the value of k is small. We show the feasibility of our approach through experiments on real census data |
| Sponsorship | IEEE CPS |
| Starting Page | 518 |
| Ending Page | 523 |
| File Size | 283194 |
| Page Count | 6 |
| File Format | |
| ISBN | 0769527027 |
| DOI | 10.1109/ICDMW.2006.127 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer science Data privacy Costs Publishing Taxonomy Government Loss measurement Virtual manufacturing Yarn Protection |
| Content Type | Text |
| Resource Type | Article |
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