Privacy-Preserving Natural Language Processing

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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.