Privacy Preserving AI and Deep Learning Architectures for Agile Business Process Integration in Cloud Based IoT Networks
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Abstract
The rapid convergence of Artificial Intelligence (AI), Deep Learning (DL), Cloud Computing, and the Internet of Things (IoT) is reshaping enterprise ecosystems into highly connected, data-driven environments. However, agile business process integration across cloud-based IoT networks introduces complex challenges related to scalability, latency, interoperability, and data privacy. This research proposes privacy-preserving AI and deep learning architectures designed to support agile business process integration in distributed cloud IoT systems. The framework integrates edge intelligence, federated learning, differential privacy, secure multi-party computation, and blockchain-enabled auditing mechanisms to ensure secure and adaptive workflow orchestration. Deep learning models are embedded within edge-cloud infrastructures to enable predictive analytics, anomaly detection, and dynamic decision optimization while maintaining data confidentiality. The proposed architecture emphasizes privacy-by-design principles, ensuring compliance with data protection regulations and minimizing data exposure risks. Experimental simulations evaluate system performance based on latency, scalability, model accuracy, and privacy leakage metrics. Results indicate significant improvements in real-time responsiveness, secure data sharing, and operational agility compared to conventional centralized architectures. This study contributes a scalable, intelligent, and privacy-aware integration model that enhances enterprise resilience, digital transformation, and trust in cloud-based IoT environments.
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