Biblio
In the wake of diversity of service requirements and increasing push for extreme efficiency, adaptability propelled by machine learning (ML) a.k.a self organizing networks (SON) is emerging as an inevitable design feature for future mobile 5G networks. The implementation of SON with ML as a foundation requires significant amounts of real labeled sample data for the networks to train on, with high correlation between the amount of sample data and the effectiveness of the SON algorithm. As generally real labeled data is scarce therefore it can become bottleneck for ML empowered SON for unleashing their true potential. In this work, we propose a method of expanding these sample data sets using Generative Adversarial Networks (GANs), which are based on two interconnected deep artificial neural networks. This method is an alternative to taking more data to expand the sample set, preferred in cases where taking more data is not simple, feasible, or efficient. We demonstrate how the method can generate large amounts of realistic synthetic data, utilizing the GAN's ability of generation and discrimination, able to be easily added to the sample set. This method is, as an example, implemented with Call Data Records (CDRs) containing the start hour of a call and the duration of the call, in minutes taken from a real mobile operator. Results show that the method can be used with a relatively small sample set and little information about the statistics of the true CDRs and still make accurate synthetic ones.
Generating synthetic data is a well-known option to limit disclosure risk in sensitive data releases. The usual approach is to build a model for the population and then generate a synthetic data set solely based on the model. We argue that building an accurate population model is difficult and we propose instead to approximate the original data as closely as privacy constraints permit. To enforce an ex ante privacy level when generating synthetic data, we introduce a new privacy model called $ε$ synthetic privacy. Then, we describe a synthetic data generation method that satisfies $ε$-synthetic privacy. Finally, we evaluate the utility of the synthetic data generated with our method.
Machine learning and data mining algorithms typically assume that the training and testing data are sampled from the same fixed probability distribution; however, this violation is often violated in practice. The field of domain adaptation addresses the situation where this assumption of a fixed probability between the two domains is violated; however, the difference between the two domains (training/source and testing/target) may not be known a priori. There has been a recent thrust in addressing the problem of learning in the presence of an adversary, which we formulate as a problem of domain adaption to build a more robust classifier. This is because the overall security of classifiers and their preprocessing stages have been called into question with the recent findings of adversaries in a learning setting. Adversarial training (and testing) data pose a serious threat to scenarios where an attacker has the opportunity to ``poison'' the training or ``evade'' on the testing data set(s) in order to achieve something that is not in the best interest of the classifier. Recent work has begun to show the impact of adversarial data on several classifiers; however, the impact of the adversary on aspects related to preprocessing of data (i.e., dimensionality reduction or feature selection) has widely been ignored in the revamp of adversarial learning research. Furthermore, variable selection, which is a vital component to any data analysis, has been shown to be particularly susceptible under an attacker that has knowledge of the task. In this work, we explore avenues for learning resilient classification models in the adversarial learning setting by considering the effects of adversarial data and how to mitigate its effects through optimization. Our model forms a single convex optimization problem that uses the labeled training data from the source domain and known- weaknesses of the model for an adversarial component. We benchmark the proposed approach on synthetic data and show the trade-off between classification accuracy and skew-insensitive statistics.