论文标题
指纹语言生成的匿名文本
De-Anonymizing Text by Fingerprinting Language Generation
论文作者
论文摘要
机器学习系统的组件尚未被视为安全热点。 ML开发人员尚未采用安全的编码实践，例如确保没有执行路径取决于机密输入。我们通过调查核采样的方式来启动ML系统的代码安全性---一种流行的生成文本方法，用于诸如自动完成之类的应用程序 - - 不知不觉地泄漏了用户键入的文本。我们的主要结果是，许多天然英语单词序列的一系列核大小是独特的指纹。然后，我们展示攻击者如何通过通过合适的侧渠道（例如缓存访问时间）测量这些指纹来推断键入文本，解释该攻击如何帮助匿名化匿名文本并讨论防御。
Components of machine learning systems are not (yet) perceived as security hotspots. Secure coding practices, such as ensuring that no execution paths depend on confidential inputs, have not yet been adopted by ML developers. We initiate the study of code security of ML systems by investigating how nucleus sampling---a popular approach for generating text, used for applications such as auto-completion---unwittingly leaks texts typed by users. Our main result is that the series of nucleus sizes for many natural English word sequences is a unique fingerprint. We then show how an attacker can infer typed text by measuring these fingerprints via a suitable side channel (e.g., cache access times), explain how this attack could help de-anonymize anonymous texts, and discuss defenses.
