通过消息内容预测收件人特征，揭示新型隐私泄露风险
Recipient Profiling: Predicting Characteristics from Messages
- 从通信文本中推断收件人性别、年龄等属性
- 在多个公开数据集上实现有效预测，准确率显著高于随机水平
- 模型具备跨数据集迁移能力，适合隐私安全研究者参考
研究表明，文本可能无意中暴露作者的敏感信息，如性别或年龄，引发严重隐私担忧。现有工作多聚焦于隐藏作者信息，但本文指出：当文本为个人间通信时，收件人同样面临隐私泄露风险。为此，我们提出并研究「收件人画像」问题——即从消息内容预测收件人特征。我们在多个公开数据集（https://huggingface.co/datasets/sileod/recipient_profiling）上提供了实证证据，证明该任务具有可行性。此外，我们发现训练出的模型可迁移至其他数据集，尽管准确率有所下降。
原文摘要 · Abstract (English)
It has been shown in the field of Author Profiling that texts may inadvertently reveal sensitive information about their authors, such as gender or age. This raises important privacy concerns that have been extensively addressed in the literature, in particular with the development of methods to hide such information. We argue that, when these texts are in fact messages exchanged between individuals, this is not the end of the story. Indeed, in this case, a second party, the intended recipient, is also involved and should be considered. In this work, we investigate the potential privacy leaks affecting them, that is we propose and address the problem of Recipient Profiling. We provide empirical evidence that such a task is feasible on several publicly accessible datasets (https://huggingface.co/datasets/sileod/recipient_profiling). Furthermore, we show that the learned models can be transferred to other datasets, albeit with a loss in accuracy.
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