Privacy- Enhanced Fungal Infection Detection: Leveraging Differential Privacy and Federated Learning in Healthcare System

In the era of big data, safeguarding the privacy and security of sensitive healthcare information is predominant. This research paper investigates the integration of differential privacy and federated learning to create a robust framework for privacy-preserving analysis of fungal infection data. The proposed framework ensures the confidentiality of individual patient data while enabling collaborative analysis across multiple healthcare organizations. Differential privacy mechanisms are employed to provide strong privacy guarantees, ensuring that the inclusion of individual data does not compromise overall privacy. Federated learning facilitates decentralized data processing, minimizing the risk of data breaches by keeping data on local premises. Extensive experiments and simulations were conducted using real-world fungal infection datasets to assess the framework's effectiveness and feasibility. The results indicate that the framework effectively preserves data privacy while maintaining better performance metrics in fungal infection detection. The framework demonstrated a significant reduction in privacy risks without compromising the quality of the analytical outcomes. This study's findings contribute to advancing privacy-preserving methodologies in healthcare data analysis, promoting secure data-sharing and collaborative efforts within the healthcare system.