Data mining technology has attracted significant interest as a means of identifying patterns and trends from large collections of data. It is however evident that the collection and analysis of data that include personal information may violate the privacy of the individuals to whom information refers. Privacy protection in data mining is then becoming a crucial issue that has captured the attention of many researchers. In this chapter, we first describe the concept of k-anonymity and illustrate different approaches for its enforcement. We then discuss how the privacy requirements characterized by k-anonymity can be violated in data mining and introduce possible approaches to ensure the satisfaction of k-anonymity in data mining.
K-anonymous data mining : a survey / V. Ciriani, S. De Capitani di Vimercati, S. Foresti, P. Samarati - In: Privacy-preserving data mining : models and algorithms / [a cura di] C.C. Aggarwal, P.S. Yu. - New York : Springer, 2008. - ISBN 9780387709918. - pp. 105-136
K-anonymous data mining : a survey
V. CirianiPrimo
;S. De Capitani di VimercatiSecondo
;S. ForestiPenultimo
;P. SamaratiUltimo
2008
Abstract
Data mining technology has attracted significant interest as a means of identifying patterns and trends from large collections of data. It is however evident that the collection and analysis of data that include personal information may violate the privacy of the individuals to whom information refers. Privacy protection in data mining is then becoming a crucial issue that has captured the attention of many researchers. In this chapter, we first describe the concept of k-anonymity and illustrate different approaches for its enforcement. We then discuss how the privacy requirements characterized by k-anonymity can be violated in data mining and introduce possible approaches to ensure the satisfaction of k-anonymity in data mining.Pubblicazioni consigliate
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