Differential Privacy

This work identifies federated learning’s communication topology as a privacy channel orthogonal to DP-SGD, and derives a topology-aware noise allocation that provably dominates uniform DP-SGD on the per-client leakage bound at no measurable utility cost.

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.