A hierarchical IoT-Edge-Cloud system with hybrid Laplace-Gaussian differential privacy on K-means, logistic regression, random forest and naive Bayes models reaches 80-81% accuracy at ε=5 while cutting attribute inference attacks by 18% and data reconstruction correlation by 70%, plus 8× latency cut
Blockchain-enabled federated learning
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Differential Privacy for Secure Machine Learning in Healthcare IoT-Cloud Systems
A hierarchical IoT-Edge-Cloud system with hybrid Laplace-Gaussian differential privacy on K-means, logistic regression, random forest and naive Bayes models reaches 80-81% accuracy at ε=5 while cutting attribute inference attacks by 18% and data reconstruction correlation by 70%, plus 8× latency cut