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Capturing data usefulness and privacy protection in K-anonymisation by Grigorios Loukides; Jianhua Shao is a scholarly article available to read on EtoBox.

What is Capturing data usefulness and privacy protection in K-anonymisation about?

K-anonymisation is an approach to protecting privacy contained within a data set. A good k-anonymisation algorithm should anonymise a data set in such a way that private information contained within it is hidden, yet anonymised data is still useful in intended applications. Maximising both data usefulness and privacy protection in k-anonymisation is however difficult. In this paper, we suggest a metric that attempts to quantify these two properties and introduce a clustering based algorithm that can achieve a balance between them in k-anonymisation.

Author

Grigorios Loukides; Jianhua Shao

Publisher

ACM

Published

2007

Language

EN