A new approach to preserving privacy data mining based on fuzzy theory
Run Cui, 김형중 · 한국정보통신설비학회 학술대회 · 2013
With the rapid development of information techniques, data mining approach has become one of the most important tools to discover the in-deep associations of tuples in the big data sets. So how to protect the private information is quite a huge challenge, especially during the data mining procedure. In this paper, we provide a new method to protect the private information based on fuzzy set theory. This new style of expression can provide more details of the anonymity subsets without reducing the security. And an experiment is provided to show that this approach is suitable for the classification. In the future, this approach can be adapted to the data stream as the low computation complexity of the fuzzy function.