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Classifying instances in evolving data stream is a challenging task because of its properties, e.g., infinite length, concept drift, and concept evolution. Most of the currently available approaches to classify stream data instances divide the stream data into fixed size chunks to fit the data in memory and process the fixed size chunk one after another. However, this may lead to failure of capturing...
Perturbation method is a very important technique in privacy preserving data mining. In this technique, loss of information versus preservation of privacy is always a trade off. The question is, how much are the users willing to compromise their privacy? This is a choice that changes from individual to individual. In this paper, we propose an individually adaptable perturbation model, which enables...
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