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» Using Anonymized Data for Classification
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KDD
2006
ACM
130views Data Mining» more  KDD 2006»
16 years 28 days ago
Efficient anonymity-preserving data collection
The output of a data mining algorithm is only as good as its inputs, and individuals are often unwilling to provide accurate data about sensitive topics such as medical history an...
Justin Brickell, Vitaly Shmatikov
KDD
2003
ACM
148views Data Mining» more  KDD 2003»
16 years 28 days ago
Mining concept-drifting data streams using ensemble classifiers
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud...
Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han
ICDE
2008
IEEE
124views Database» more  ICDE 2008»
16 years 1 months ago
Privacy: Theory meets Practice on the Map
In this paper, we propose the first formal privacy analysis of a data anonymization process known as the synthetic data generation, a technique becoming popular in the statistics c...
Ashwin Machanavajjhala, Daniel Kifer, John M. Abow...
GIS
2008
ACM
16 years 1 months ago
Privacy: preserving trajectory collection
In order to provide context?aware Location?Based Services, real location data of mobile users must be collected and analyzed by spatio?temporal data mining methods. However, the d...
Gyözö Gidófalvi, Torben Bach Pede...
AUSAI
2008
Springer
15 years 2 months ago
Cross-Domain Knowledge Transfer Using Semi-supervised Classification
Traditional text classification algorithms are based on a basic assumption: the training and test data should hold the same distribution. However, this identical distribution assum...
Yi Zhen, Chunping Li