Author(s) |
Islam, Md Zahidul
Brankovic, Ljiljana
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Publication Date |
2011-12
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Abstract |
<p>During the whole process of data mining (from data collection to knowledge discovery) various sensitive data get exposed to several parties including data collectors, cleaners, preprocessors, miners and decision-makers. The exposure of sensitive data can potentially lead to breach of individual privacy. Therefore, many privacy preserving techniques have been proposed recently. In this paper we present a framework that uses a few novel noise addition techniques for protecting individual privacy while maintaining a high data quality. We add noise to all attributes, both numerical and categorical. We present a novel technique for clustering categorical values and use it for noise addition purpose. A security analysis is also presented for measuring the security level of a data set.</p>
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Citation |
Knowledge-Based Systems, 24(8), p. 1214-1223
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ISSN |
1872-7409
0950-7051
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Link | |
Publisher |
Elsevier BV
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Title |
Privacy preserving data mining: A noise addition framework using a novel clustering technique
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Type of document |
Journal Article
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Entity Type |
Publication
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