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
Nist special publication 800 nist sp 800-66r2 im- plementing the health insurance portability and accountability act (hipaa) security rule a cybersecurity resource guide
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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