Image and Attribute Based Convolutional Neural Network Inference Attacks in Social Networks
Image and Attribute Based Convolutional Neural Network Inference Attacks in Social Networks
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DOI：
10.1109/tnse.2018.2797930
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发表时间：
2020-04-01
影响因子：
6.6
通讯作者：
Sun, Yunchuan
中科院分区：
文献类型：
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作者：
Mei, Bo;Xiao, Yinhao;Sun, Yunchuan
在现代社会中，社交网络对在线用户起着重要作用。然而，在这些服务蓬勃发展的背后，一个不可回避的问题是隐私问题。与此同时，神经网络近年来得到了迅速发展，并被证明在推理攻击中非常有效。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的最先进的卷积神经网络（CNN）模型。因此，无论用户是否具有合法的配置文件图像，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用于敏感信息预测。除了框架之外，本文还展示了用于推理攻击的全连接神经网络（FCNN）的详细配置。这一部分在现有的研究中通常是缺失的。此外，传统的机器学习算法的实现，以比较从构造的FCNN的结果。最后，本文还讨论了在社交网络中应用差分隐私（DP）可以有效地破坏推理攻击的准确性。
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.