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http://dx.doi.org/10.3745/KTCCS.2020.9.1.9

Experiment and Implementation of a Machine-Learning Based k-Value Prediction Scheme in a k-Anonymity Algorithm  

Muh, Kumbayoni Lalu (금오공과대학교 IT융복합공학과)
Jang, Sung-Bong (금오공과대학교 산학협력단)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.9, no.1, 2020 , pp. 9-16 More about this Journal
Abstract
The k-anonymity scheme has been widely used to protect private information when Big Data are distributed to a third party for research purposes. When the scheme is applied, an optimal k value determination is one of difficult problems to be resolved because many factors should be considered. Currently, the determination has been done almost manually by human experts with their intuition. This leads to degrade performance of the anonymization, and it takes much time and cost for them to do a task. To overcome this problem, a simple idea has been proposed that is based on machine learning. This paper describes implementations and experiments to realize the proposed idea. In thi work, a deep neural network (DNN) is implemented using tensorflow libraries, and it is trained and tested using input dataset. The experiment results show that a trend of training errors follows a typical pattern in DNN, but for validation errors, our model represents a different pattern from one shown in typical training process. The advantage of the proposed approach is that it can reduce time and cost for experts to determine k value because it can be done semi-automatically.
Keywords
Artificial Neural Networks; k-Anonymity; k Value Prediction; TensorFlow;
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