"Privacy-Enhanced AI for Natural Language Processing"

Abstract

Privacy concerns in the era of ubiquitous data collection have become paramount, especially with the rise of artificial intelligence (AI) applications like Natural Language Processing (NLP). This paper explores various methodologies and techniques aimed at enhancing privacy in NLP tasks. We examine the challenges posed by the collection and utilization of sensitive data, such as personal communications and identifiable information, in training and deploying NLP models. Key approaches discussed include differential privacy, federated learning, homomorphic encryption, and secure multi-party computation. The paper also evaluates the trade-offs between privacy protection and model performance, highlighting advancements in preserving data privacy without compromising the utility of AI models. Finally, we discuss future directions and potential areas for research in the evolving landscape of privacy-enhanced AI for NLP.