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Erschienen in:
2008 | OriginalPaper | Buchkapitel
A General Survey of Privacy-Preserving Data Mining Models and Algorithms
verfasst von : Charu C. Aggarwal, Philip S. Yu
Erschienen in: Privacy-Preserving Data Mining
Verlag: Springer US
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In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization,
k
-anonymization, and distributed privacy-preserving data mining. We also discuss cases in which the output of data mining applications needs to be sanitized for privacy-preservation purposes. We discuss the computational and theoretical limits associated with privacy-preservation over high dimensional data sets.