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Content Provider | IEEE Xplore Digital Library |
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Author | Yongcheng Luo Yaqian Jiang Jiajin Le |
Copyright Year | 2011 |
Description | Author affiliation: College of Information Science and Technology, Donghua University, Shanghai, China (Jiajin Le) || Library & College of Information, Science and Technology, Donghua, University, Shanghai, China (Yongcheng Luo) || Library, Donghua University, Shanghai, China (Yaqian Jiang) |
Abstract | We present a new self-adaptation data publishing framework based on the concept of personalized anonymity for a certain application field (e.g. digital library). Our technique applies a self-adaptive mechanism to meet the needs of the different data applications with the minimum generalization for satisfying everybody's requirements, and thus, retains the largest amount of information from the metadata. We propose an algorithm framework for computing a generalized table with small information loss, which guarantees the appropriate breach probability for each tuple under the guidance of domain knowledge. The strategy of the sensitive attribute generalization and quasi-attribute generalization is well applied in the algorithm. |
Starting Page | 2391 |
Ending Page | 2394 |
File Size | 213090 |
Page Count | 4 |
File Format | |
ISBN | 9781612847191 |
e-ISBN | 9781612847221 |
DOI | 10.1109/MEC.2011.6025974 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-08-19 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Algorithm design and analysis Data privacy Taxonomy Domain knowledge Anonymity Educational institutions Data publishing Generalization Algorithm Privacy Publishing Self-adaptation Libraries |
Content Type | Text |
Resource Type | Article |
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