Differential Privacy

Differential Privacy for Secure Machine Learning in Healthcare IoT-Cloud Systems

Under Review

This paper proposes a multi-layer IoT-Edge-Cloud healthcare architecture combining differential privacy with a hybrid Laplace-Gaussian noise mechanism, achieving 82-84% ML accuracy while reducing inference attacks by up to 70%, with blockchain for data integrity and edge computing delivering 8× latency reduction for emergencies.

Network Structures as an Attack Surface: Topology-Based Privacy Leakage in Federated Learning

Preprint

This paper identifies network topology as a fundamental privacy vulnerability in federated learning systems, demonstrating that adversaries can infer sensitive data distribution patterns through communication patterns, parameter magnitudes, and structural correlations even under strong differential privacy guarantees.