论文标题
通过功能说明提取私人图形提取
Private Graph Extraction via Feature Explanations
论文作者
论文摘要
隐私和解释性是实现值得信赖的机器学习的两种重要要素。我们通过图形重建攻击研究了图机学习中这两个方面的相互作用。这里的对手的目的是重建训练数据的图形结构，以访问模型说明。基于对手可用的不同类型的辅助信息，我们提出了几种图形重建攻击。我们表明，事后功能解释的其他知识大大提高了这些攻击的成功率。此外，我们详细研究了攻击性能相对于三种不同类别的图形神经网络的解释方法的差异：基于梯度，基于扰动和基于替代模型的方法。虽然基于梯度的解释在图形结构方面显示出最大的解释，但我们发现这些解释并不总是能获得效用高。对于其他两个类别的解释，隐私泄漏随着解释实用程序的增加而增加。最后，我们根据释放解释的随机响应机制提出了一种防御，从而大大降低了攻击成功率。我们的代码可从https://github.com/iyempissy/graph-stealing-attacks-with-with-planation获得
Privacy and interpretability are two important ingredients for achieving trustworthy machine learning. We study the interplay of these two aspects in graph machine learning through graph reconstruction attacks. The goal of the adversary here is to reconstruct the graph structure of the training data given access to model explanations. Based on the different kinds of auxiliary information available to the adversary, we propose several graph reconstruction attacks. We show that additional knowledge of post-hoc feature explanations substantially increases the success rate of these attacks. Further, we investigate in detail the differences between attack performance with respect to three different classes of explanation methods for graph neural networks: gradient-based, perturbation-based, and surrogate model-based methods. While gradient-based explanations reveal the most in terms of the graph structure, we find that these explanations do not always score high in utility. For the other two classes of explanations, privacy leakage increases with an increase in explanation utility. Finally, we propose a defense based on a randomized response mechanism for releasing the explanations, which substantially reduces the attack success rate. Our code is available at https://github.com/iyempissy/graph-stealing-attacks-with-explanation
