Differential Privacy

This work identifies hierarchical federated learning’s aggregation topology as a privacy channel orthogonal to DP-SGD, and derives a topology-aware noise allocation that provably matches uniform DP-SGD’s per-client privacy guarantee while recovering up to 88% of the noise budget.

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.