Application of Machine Learning-NLP Approach with Fully Homomorphic Encryption Techniques in Medical PII Data

Integrating machine learning (ML) into healthcare systems has revolutionized diagnostic capabilities and personalized treatment plans. However, using sensitive medical data introduces significant privacy concerns, necessitating secure computational frameworks. Privacy-preserving machine learning (PPML) addresses these challenges by enabling data processing without exposing sensitive information. This study explores the application of Fully Homomorphic Encryption (FHE) with machine learning models for classifying medical dialogues containing potentially sensitive personally identifiable information (PII). Benchmarking FHE-based inference models against the traditional plaintext approaches. This study demonstrates that FHE can achieve comparable ensuring robust data privacy. However, enhanced security comes at the cost of increased computational time and memory usage. The study highlights a critical trade-off between maintaining high-speed inference and preserving privacy, providing valuable insights into medical data. These results guide decision-makers in evaluating the deployment of secure, FHE-based solutions in production environments. This study explores integrating privacy-preserving techniques with machine learning and outlines future research directions.