Fake detection in imbalance dataset by Semi-supervised learning with GAN

As social media grows faster, harassment becomes more prevalent which leads to considered fake detection a fascinating field among researchers. The graph nature of data with the large number of nodes caused different obstacles including a considerable amount of unrelated features in matrices as high dispersion and imbalance classes in the dataset. To deal with these issues Auto-encoders and a combination of semi-supervised learning and the GAN algorithm which is called SGAN were used. This paper is deploying a smaller number of labels and applying SGAN as a classifier. The result of this test showed that the accuracy had reached 91\% in detecting fake accounts using only 100 labeled samples.

Single SMPC Invocation DPHelmet: Differentially Private Distributed Learning on a Large Scale

Distributing machine learning predictors enables the collection of large-scale datasets while leaving sensitive raw data at trustworthy sites. We show that locally training support vector machines (SVMs) and computing their averages leads to a learning technique that is scalable to a large number of users, satisfies differential privacy, and is applicable to non-trivial tasks, such as CIFAR-10. For a large number of participants, communication cost is one of the main challenges. We achieve a low communication cost by requiring only a single invocation of an efficient secure multiparty summation protocol. By relying on state-of-the-art feature extractors (SimCLR), we are able to utilize differentially private convex learners for non-trivial tasks such as CIFAR-10. Our experimental results illustrate that for $1{,}000$ users with $50$ data points each, our scheme outperforms state-of-the-art scalable distributed learning methods (differentially private federated learning, short DP-FL) while requiring around $500$ times fewer communication costs: For CIFAR-10, we achieve a classification accuracy of $79.7\,\%$ for an $\varepsilon = 0.59$ while DP-FL achieves $57.6\,\%$. More generally, we prove learnability properties for the average of such locally trained models: convergence and uniform stability. By only requiring strongly convex, smooth, and Lipschitz-continuous objective functions, locally trained via stochastic gradient descent (SGD), we achieve a strong utility-privacy tradeoff.

Detecting fake accounts through Generative Adversarial Network in online social media

Nowadays, online social media has become an inseparable part of human life, also this phenomenon is being used by individuals to send messages and share files via videos and images. Twitter, Instagram, and Facebook are well-known samples of these networks. One of the main challenges of privacy for users in these networks is anomalies in security. Anomalies in online social networks can be attributed to illegal behavior, such deviance is done by malicious people like account forgers, online fraudsters, etc. This paper proposed a new method to identify fake user accounts by calculating the similarity measures among users, applying the Generative Adversarial Network (GAN) algorithm over the Twitter dataset. The results of the proposed method showed, accuracy was able to reach 98.1% for classifying and detecting fake user accounts.