使用神经生成模型发布降低用户风格识别性的合成Twitter语料库
Using Neural Generative Models to Release Synthetic Twitter Corpora with Reduced Stylometric Identifiability of Users
摘要：
我们提出了一种使用神经生成模型生成Twitter数据的合成版本的方法。其目标是保护源数据中的个人免受样式重新识别攻击，同时仍然发布具有研究价值的数据。具体来说，我们生成的推文语料库，保持用户级的单词分布，通过增强神经语言模型与用户特定的组件。我们比较我们的方法，两个标准的文本数据保护方法：编校和迭代翻译。我们评估了三种方法的风险和效用的措施。我们定义风险的重新识别的风格模型，我们定义两个一般的字分布措施和两个常见的文本分析研究任务的基础上的效用。我们发现，神经模型能够显着降低风险，比以前的方法几乎没有成本的效用。我们还证明了神经模型允许数据提供者通过模型调整参数来主动控制风险-效用权衡。这项工作为一种新工具提供了有希望的结果，该工具解决了自由文本的隐私问题，并以尊重隐私和道德责任的方式共享社交媒体数据。
英文摘要：
We present a method for generating synthetic versions of Twitter data using neural generative models. The goal is protecting individuals in the source data from stylometric re-identification attacks while still releasing data that carries research value. Specifically, we generate tweet corpora that maintain user-level word distributions by augmenting the neural language models with user-specific components. We compare our approach to two standard text data protection methods: redaction and iterative translation. We evaluate the three methods on measures of risk and utility. We define risk following the stylometric models of re-identification, and we define utility based on two general word distribution measures and two common text analysis research tasks. We find that neural models are able to significantly lower risk over previous methods with little cost to utility. We also demonstrate that the neural models allow data providers to actively control the risk-utility trade-off through model tuning parameters. This work presents promising results for a new tool addressing the problem of privacy for free text and sharing social media data in a way that respects privacy and is ethically responsible.
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