Privacy-Preserving Natural Language Processing for Clinical Notes

The increasing adoption of Natural Language Processing (NLP) in healthcare has the potential to transform clinical practices by enabling the efficient extraction of insights from unstructured clinical notes. However, the sensitive nature of patient information contained within these notes raises significant privacy concerns, necessitating robust privacy-preserving methods. This paper explores the integration of privacy-preserving techniques in NLP applications designed for clinical notes, addressing the dual objectives of maintaining patient confidentiality and leveraging the rich data for clinical decision-making. We begin by reviewing existing privacy regulations and the ethical implications of handling sensitive healthcare data. The study then examines various privacy-preserving methodologies, including differential privacy, federated learning, and homomorphic encryption, highlighting their applicability in the context of NLP. Empirical evaluations demonstrate the effectiveness of these techniques in safeguarding patient information while preserving the utility of NLP models. The findings underscore the importance of developing privacy-aware NLP frameworks that balance the need for data-driven insights with stringent privacy requirements. By proposing a comprehensive approach to privacy-preserving NLP in clinical settings, this research contributes to the ongoing discourse on ethical AI deployment in healthcare, ultimately fostering greater trust and security in the use of advanced analytics for patient care.