Privacy-Preserving Natural Language Processing

法律规定，我们的大多数个人身份信息都必须受到收集这些信息的企业和主权实体的保护。这些信息有很大一部分是以文本和/或语音形式提供的。随着自然语言处理变得越来越精确，对来自各种来源的训练数据的依赖程度越来越高，泄露私人信息的可能性也越来越大。例如，移动电话不应该按照字符预测任务的建议完成提供个人信息，即使在完成有意义的情况下也是如此。这一研究方案寻求将同态加密、数值分析和隐私保护机器学习的研究与自然语言处理的最新进展结合起来，以制定用于处理文本和语音的协议和算法，并保证防止私人信息泄露。这些结果的潜在应用与关于几个任务的已经大量的文献重叠，例如生物医学NLP/语音或语音处理的安全应用，但提供了比绝大多数通用NLP方法所提供的更高水平的技术复杂性。这项研究还提供了一个宝贵的机会来推测一系列现实的、相互关联的用例，这些用例促使技术更广泛地适用于隐私保护计算-尽管到目前为止，这些技术仍然躲避了密码学和安全界的注意。

Most of our personally identifiable information is required by law to be protected by the businesses and sovereign entities that collect it. A great deal of that information is available in text and/or speech. As natural language processing becomes more precise, and increasingly more dependent upon training data from a variety of sources, the potential for compromising private information grows. Mobile phones, for example, should not offer personal information as suggested completions to character prediction tasks, even in where the completions make sense. This research programme seeks to combine research in homomorphic encryption, numerical analysis and privacy-preserving machine learning with recent advances in natural language processing to formulate protocols and algorithms for processing text and speech with guarantees that prevent the disclosure of private information. The potential applications of these results overlap with the already substantial body of literature on several tasks, e.g., biomedical NLP/speech, or security applications of speech processing, but offers a greater level of technical sophistication than would be offered by the vast majority of generic NLP approaches. This research also offers a valuable opportunity to speculate on a range of realistic, interconnected use cases that motivate techniques with broader applicability to privacy-preserving computing in general ---- techniques that have, to date, nevertheless evaded the attention of the cryptography and security communities.