Privacy-Preserving Self-Supervised Learning for Secure Image Processing: A BYOL-Based Framework for MNIST and Chest X-ray Data

The increasing use of high-dimensional imaging in medical AI raises significant privacy and security concerns. This paper presents a Bootstrap Your Own Latent (BYOL)-based self-supervised learning (SSL) framework for secure image processing, ensuring compliance with HIPAA and privacy-preserving machine learning (PPML) techniques. Our method integrates federated learning, homomorphic encryption, and differential privacy to enhance security while reducing dependence on labeled data. Experimental results on the MNIST and NIH Chest X-ray datasets demonstrate a classification accuracy of 97.5% and 99.99% (pre-fine-tuning 40%), with improved clustering performance using K-Means (Silhouette Score: 0.5247). These findings validate BYOL’s capability for robust, privacy-preserving image processing while emphasizing the need for fine-tuning to optimize classification performance.