Rényi Differential Privacy Analysis of Skellam Under Federated Learning in Internet of Health Things

Preserving privacy is a critical challenge when applying federated learning (FL) to sensitive healthcare data in the Internet of Health Things (IoHT) applications. Despite employing a range of differential privacy (DP) analysis methods to assess privacy in FL, achieving an optimal balance between privacy and utility continues to be challenging. An incorrect balance in the privacy-utility tradeoff can significantly compromise patient privacy and affect the reputation of healthcare organizations. To address this issue, this research investigates a robust privacy-preserving framework utilising the Skellam mechanism and Renyi Differential Privacy (RDP) technique. By utilizing the closed-under summation property of the Skellam mechanism, we address the challenges in aggregating noisy updates in FL. Further, through a rigorous RDP analysis, we demonstrate that selecting an appropriate RDP order <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> and privacy budget ∊ enables a balanced tradeoff within the FL setup. Finally, we validate our approach through the development of a novel algorithm, DP-DotGAT. The algorithm takes function call graphs (FCGs) obtained from healthcare applications as input. Additionally, it effectively protects against membership inference attacks while maintaining an accuracy range of 70% to 73%. Our results indicate that this approach greatly improves the privacy of health care data and can lead to wider adoption of secure and privacy-preserving machine learning models in healthcare sectors.