GN: A privacy preserving data publishing method based on generalization and noise techniques | IEEE Conference Publication | IEEE Xplore

GN: A privacy preserving data publishing method based on generalization and noise techniques


Abstract:

Generalization is a popular technique to realize k-anonymity. However, when the distribution of original data is uneven, generalization will distort the data greatly, whi...Show More

Abstract:

Generalization is a popular technique to realize k-anonymity. However, when the distribution of original data is uneven, generalization will distort the data greatly, which makes the anonymous data low utility. To address the problem, we propose a GN method, which limits the degree of generalization by adding noise tuples during anonymization. We also propose a GN-Bottom-up algorithm to achieve k-anonymity based on GN method. Experiments show that the GN method can generate anonymous data with less distortion and higher classification accuracy than generalization method.
Date of Conference: 13-15 December 2013
Date Added to IEEE Xplore: 17 February 2014
Electronic ISBN:978-1-4799-1282-7
Conference Location: Beijing, China

References

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