Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

中文翻译：

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使用机器学习异常检测保护多媒体社交网络中的隐私

如今，用户隐私已成为多媒体社交网络中的关键问题。但是，依靠用户的日志文件和行为模式的传统机器学习异常检测技术不足以保留它。因此，社交网络安全性应具有多种安全措施，以考虑到附加信息来保护用户数据。更准确地说，访问控制模型可以在隐私保护过程中补充机器学习算法。这些模型可以使用从用户的配置文件中得出的更多信息来检测异常用户。在本文中，我们实现了一种隐私保护算法，该算法将有监督和无监督的机器学习异常检测技术与访问控制模型相结合。由于政策丰富而细密，我们的控制模型会不断更新用于分类用户的属性列表。它已经在真实数据集上成功进行了测试，使用贝叶斯分类器的准确率超过95％，使用深度神经网络和长短期记忆递归神经网络分类器的接收器工作特性曲线的准确率达到95.53％。实验结果表明，该方法优于其他检测技术，例如支持向量机，隔离林，主成分分析和Kolmogorov–Smirnov检验。