文本的隐私保护神经表示
Privacy-preserving Neural Representations of Text
摘要：
本文在隐私保护的背景下讨论了针对自然语言处理（NLP）的深度学习系统的对抗攻击。我们研究了一种特定类型的攻击：攻击者窃听神经文本分类器的隐藏表示，并试图恢复有关输入文本的信息。这种场景可能出现在当神经网络的计算跨多个设备共享时的情况下，例如，某些隐藏表示由用户的设备计算并发送到基于云的模型。我们测量的隐私的隐藏表示的攻击者准确预测特定的私人信息，从它和特征的隐私和实用程序的神经表示之间的权衡的能力。最后，我们提出了几种基于修改后的训练目标的防御方法，并表明它们提高了神经表征的隐私性。
英文摘要：
This article deals with adversarial attacks towards deep learning systems for Natural Language Processing (NLP), in the context of privacy protection. We study a specific type of attack: an attacker eavesdrops on the hidden representations of a neural text classifier and tries to recover information about the input text. Such scenario may arise in situations when the computation of a neural network is shared across multiple devices, e.g. some hidden representation is computed by a user's device and sent to a cloud-based model. We measure the privacy of a hidden representation by the ability of an attacker to predict accurately specific private information from it and characterize the tradeoff between the privacy and the utility of neural representations. Finally, we propose several defense methods based on modified training objectives and show that they improve the privacy of neural representations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
