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

Deep learning (DL) models for natural language processing (NLP) tasks often handle private data, demanding protection against breaches and disclosures. Data protection laws, such as the European Union\u27s General Data Protection Regulation (GDPR), thereby enforce the need for privacy. Although many privacy-preserving NLP methods have been proposed in recent years, no categories to organize them have been introduced yet, making it hard to follow the progress of the literature. To close this gap, this article systematically reviews over sixty DL methods for privacy-preserving NLP published between 2016 and 2020, covering theoretical foundations, privacy-enhancing technologies, and analysis of their suitability for real-world scenarios. First, we introduce a novel taxonomy for classifying the existing methods into three categories: data safeguarding methods, trusted methods, and verification methods. Second, we present an extensive summary of privacy threats, datasets for applications, and metrics for privacy evaluation. Third, throughout the review, we describe privacy issues in the NLP pipeline in a holistic view. Further, we discuss open challenges in privacy-preserving NLP regarding data traceability, computation overhead, dataset size, the prevalence of human biases in embeddings, and the privacy-utility tradeoff. Finally, this review presents future research directions to guide successive research and development of privacy-preserving NLP models

IT Laws in the Era of Cloud-Computing

This book documents the findings and recommendations of research into the question of how IT laws should develop on the understanding that today’s information and communication technology is shaped by cloud computing, which lies at the foundations of contemporary and future IT as its most widespread enabler. In particular, this study develops on both a comparative and an interdisciplinary axis, i.e. comparatively by examining EU and US law, and on an interdisciplinary level by dealing with law and IT. Focusing on the study of data protection and privacy in cloud environments, the book examines three main challenges on the road towards more efficient cloud computing regulation: -understanding the reasons behind the development of diverging legal structures and schools of thought on IT law -ensuring privacy and security in digital clouds -converging regulatory approaches to digital clouds in the hope of more harmonised IT laws in the future