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. © 2008 Springer Science+Business Media, LLC.
CITATION STYLE
Aggarwal, C. C., & Yu, P. S. (2008). Privacy-preserving data mining: A survey. In Handbook of Database Security: Applications and Trends (pp. 431–460). Springer US. https://doi.org/10.1007/978-0-387-48533-1_18
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