论文标题
语言模型保留隐私是什么意思？
What Does it Mean for a Language Model to Preserve Privacy?
论文作者
论文摘要
自然语言反映了我们的私人生活和身份，使其隐私问题与现实生活一样广泛。语言模型缺乏理解文本的上下文和敏感性的能力，并且倾向于记住其训练集中存在的短语。对手可以利用这种提取培训数据的趋势。根据内容的性质和收集该数据的上下文，这可能违反了隐私的期望。因此，对保护隐私的培训语言模型的技术越来越兴趣。在本文中，我们讨论了流行数据保护技术（数据消毒和差异隐私）做出的狭窄假设与自然语言和隐私作为社会规范的广泛性之间的不匹配。我们认为，现有的保护方法不能保证语言模型的隐私概念。我们得出的结论是，应该对文本数据进行培训，该文本数据是明确生产的供公众使用的。
Natural language reflects our private lives and identities, making its privacy concerns as broad as those of real life. Language models lack the ability to understand the context and sensitivity of text, and tend to memorize phrases present in their training sets. An adversary can exploit this tendency to extract training data. Depending on the nature of the content and the context in which this data was collected, this could violate expectations of privacy. Thus there is a growing interest in techniques for training language models that preserve privacy. In this paper, we discuss the mismatch between the narrow assumptions made by popular data protection techniques (data sanitization and differential privacy), and the broadness of natural language and of privacy as a social norm. We argue that existing protection methods cannot guarantee a generic and meaningful notion of privacy for language models. We conclude that language models should be trained on text data which was explicitly produced for public use.
