In modern society, social networks play an important role for online users. However, one unignorable problem behind the booming of the services is privacy issues. At the same time, neural networks have been swiftly developed in recent years, and are proven to be very effective in inference attacks. This article proposes a new framework for inference attacks in social networks, which smartly integrates and modifies the existing state-of-the-art convolutional neural network (CNN) models. As a result, the framework can fit wider applicable scenarios for inference attacks no matter whether a user has a legit profile image or not. Moreover, the framework is able to boost the existing high-accuracy CNN for sensitive information prediction. In addition to the framework, the article also shows the detailed configuration of fully connected neural networks (FCNNs) for inference attacks. This part is usually missing in the existing studies. Furthermore, traditional machine learning algorithms are implemented to compare the results from the constructed FCNN. Last but not least, this article also discusses that applying differential privacy (DP) can effectively undermine the accuracy of inference attacks in social networks.

中文翻译：

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社交网络中基于图像和属性的卷积神经网络推理攻击

在现代社会，社交网络对在线用户起着重要作用。然而，在服务蓬勃发展的背后，一个不可忽视的问题是隐私问题。同时，神经网络近年来得到了迅速发展，并被证明在推理攻击方面非常有效。本文提出了一种新的社交网络推理攻击框架，它巧妙地集成和修改了现有最先进的卷积神经网络 (CNN) 模型。因此，无论用户是否拥有合法的个人资料图片，该框架都可以适应更广泛的推理攻击适用场景。此外，该框架能够提升现有的高精度 CNN 进行敏感信息预测。除了框架，文章还展示了用于推理攻击的全连接神经网络 (FCNN) 的详细配置。这部分在现有的研究中通常是缺失的。此外，实现了传统的机器学习算法来比较构建的 FCNN 的结果。最后但同样重要的是，本文还讨论了应用差分隐私（DP）可以有效地破坏社交网络中推理攻击的准确性。