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Deep learning (DL) models for natural language processing (NLP) tasks oftenhandle private data, demanding protection against breaches and disclosures.Data protection laws, such as the European Union's General Data ProtectionRegulation (GDPR), thereby enforce the need for privacy. Although manyprivacy-preserving NLP methods have been proposed in recent years, nocategories to organize them have been introduced yet, making it hard to followthe progress of the literature. To close this gap, this article systematicallyreviews over sixty DL methods for privacy-preserving NLP published between 2016and 2020, covering theoretical foundations, privacy-enhancing technologies, andanalysis of their suitability for real-world scenarios. First, we introduce anovel taxonomy for classifying the existing methods into three categories: datasafeguarding methods, trusted methods, and verification methods. Second, wepresent an extensive summary of privacy threats, datasets for applications, andmetrics for privacy evaluation. Third, throughout the review, we describeprivacy issues in the NLP pipeline in a holistic view. Further, we discuss openchallenges in privacy-preserving NLP regarding data traceability, computationoverhead, dataset size, the prevalence of human biases in embeddings, and theprivacy-utility tradeoff. Finally, this review presents future researchdirections to guide successive research and development of privacy-preservingNLP models.

How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing