Enhancing User Privacy in Natural Language Processing (NLP) Systems

Abstract

NLP has witnessed a remarkable improvement in applications, from voice assistants to sentiment analysis and language translations. However, in this process, a huge amount of personal data flows through the NLP system. Over time, a variety of techniques and frameworks have been developed to ensure that NLP systems do not ignore user privacy. This chapter highlights the significance of privacy-enhancing technologies (differential privacy, secure multi-party computation, homomorphic encryption, federated learning, secure data aggregation, tokenization and anonymization) in protecting user privacy within NLP systems. Differential privacy introduces noise to query responses or statistical results to protect individual user privacy. Homomorphic encryption allows computations on encrypted data to maintain privacy. Federated learning facilitates collaborative model training without sharing data. Tokenization and anonymization preserve anonymity by replacing personal information with non-identifiable data. This chapter explores these methodologies and techniques for user privacy in NLP systems.