Bowonsak Seisungsittisunti

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Privacy preserving has become an essential process for any data mining task. Therefore, data transformation to ensure privacy preservation is needed. In this paper, we address a problem of privacy preserving on an incremental-data scenario in which the data need to be transformed are not static, but appended all the time. Our work is based on a well-known(More)
In the era of data explosion, privacy preserving has become a necessary task for any data mining task. Therefore, data transformation to ensure privacy preservation is needed. Meanwhile, the transformed data must have quality to be used in the intended data mining task, i.e. the impact on the data quality with regard to the data mining task must be(More)
When a data mining model is to be developed, one of the most important issues is preserving the privacy of the input data. In this paper, we address the problem of data transformation to preserve the privacy with regard to a data mining technique, associative classification, in an incremental-data scenario. We propose an incremental polynomialtime algorithm(More)
Collaboration between business partners have become crucial these days. An important issue to be addressed is data privacy. In this paper, we address a problem of data privacy based on a prominent privacy model, (k, e)-Anonymous, when a new dataset is to be released, meanwhile there might be existing datasets released elsewhere. Since some attackers might(More)
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