Implementation of Multi-Level Trust in Privacy Preserving Data Mining against Non-Linear Attack
نویسندگان
چکیده
منابع مشابه
Additive Gaussian Noise Based Data Perturbation in Multi-level Trust Privacy Preserving Data Mining
Data perturbation is one of the most popular models used in privacy preserving data mining. It is specially convenient for applications where the data owners need to export/publish the privacy-sensitive data. This work proposes that an Additive Perturbation based Privacy Preserving Data Mining (PPDM) to deal with the problem of increasing accurate models about all data without knowing exact det...
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The problem of secure distributed classification is an important one. In many situations, data is split between multiple organizations. These organizations may want to utilize all of the data to create more accurate predictive models while revealing neither their training data / databases nor the instances to be classified. The Naive Bayes Classifier is a simple but efficient baseline classifie...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2015
ISSN: 0975-8887
DOI: 10.5120/20272-2685