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Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing
حفظ حریم خصوصی دیفرانسیل حفظ مدل آموزش در داده های بزرگ بی سیم با محاسبات لبه-2018 With the popularity of smart devices and the widespread use of machine learning methods, smart edges have become the
mainstream of dealing with wireless big data. When smart edges use machine learning models to analyze wireless big data, nevertheless,
some models may unintentionally store a small portion of the training data with sensitive records. Thus, intruders can expose sensitive
information by careful analysis of this model. To solve this privacy issue, in this paper, we propose and implement a machine learning
strategy for smart edges using differential privacy. We focus our attention on privacy protection in training datasets in wireless big data
scenario. Moreover, we guarantee privacy protection by adding Laplace mechanisms, and design two different algorithms Output
Perturbation (OPP) and Objective Perturbation (OJP), which satisfy differential privacy. In addition, we consider the privacy preserving
issues presented in the existing literatures for differential privacy in the correlated datasets, and further provided differential privacy
preserving methods for correlated datasets, guaranteeing privacy by theoretical deduction. Finally, we implement the experiments on the
TensorFlow, and evaluate our strategy on four datasets, i.e., MNIST, SVHN, CIFAR-10 and STL-10. The experiment results show that our
methods can efficiently protect the privacy of training datasets and guarantee the accuracy on benchmark datasets.
Index Terms: Wireless Big Data, Smart Edges, Differential Privacy, Training Data Privacy, Machine Learning, Correlated Datasets, Laplacian Mechanism, TensorFlow |
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