Differentially Private Data Sharing in Federated Cloud Frameworks with AI Privacy Solutions
Keywords:
Federated learning, differential privacy, AI-powered privacy, data sharingAbstract
Data transport must guarantee privacy in today's networked world. Federated machine learning localizes sensitive data and enables collaborative analytics. Federated systems have trouble preserving model training data. Differential privacy (DP) protects privacy by adding noise to data or model outputs. This article discusses AI-powered privacy and differential privacy in federated cloud systems. This study analyzes how AI might enhance federated DP data exchange security, secrecy, and efficiency. The article explores AI algorithms that optimize differential privacy systems and their theoretical and practical effects. The study investigates model accuracy, computational cost, and privacy-data utility. Finally, federated cloud platforms and AI-driven privacy.
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