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“What did you say?”: Extracting unintentional secrets from predictive text learning systems

机译:“你说了什么?”:从预测文本学习系统中提取无意的秘密

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As a primary form of communication, text is used widely for online communications, including e-mail conversations, mobile text messaging, chatroom and forum discussions. Modern systems include facilities such as predictive text, recently implemented using deep learning algorithms, to estimate the next word to be written based on previous historical entries. However, we often enter sensitive information such as passwords using the same input devices - namely, smartphone soft keyboards. In this paper, we explore the problem of deep learning models which memorise sensitive training data, and how secrets can be extracted from predictive text models. We propose a general black-box attack algorithm to accomplish this for all kinds of memorised sequences, discuss mitigations and countermeasures, and explore how this attack vector could be deployed on an Android or iOS mobile device platforms as part of target reconnaissance.
机译:作为沟通的主要形式,文本广泛用于在线通信,包括电子邮件对话,移动文本消息,聊天室和论坛讨论。 现代系统包括最近使用深度学习算法实现的预测文本等设施,以估计基于以前的历史条目编写的下一个单词。 但是,我们经常使用相同的输入设备输入密码等敏感信息 - 即智能手机软键盘。 在本文中,我们探讨了记忆敏感培训数据的深度学习模型的问题,以及如何从预测文本模型中提取秘密。 我们提出了一般的黑匣子攻击算法,为各种记忆序列完成这一点,讨论减轻和对策,并探索如何在Android或IOS移动设备平台上部署该攻击矢量作为目标侦察的一部分。

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