PRIVACY-PRESERVING AI MODELS FOR CLOUD AND EDGE COMPUTING SECURITY

Abstract

The rapid adoption of cloud and edge computing has revolutionized modern digital infrastructure, enabling seamless data processing and real-time analytics. However, this shift has also introduced significant security and privacy challenges, particularly concerning the exposure of sensitive data to potential cyber threats. Traditional security frameworks often fail to provide adequate protection in distributed environments, necessitating the development of privacy-preserving AI models to safeguard critical information while maintaining computational efficiency.

This paper explores the role of privacy-preserving AI models in enhancing cloud and edge computing security, focusing on techniques such as federated learning, homomorphic encryption, secure multi-party computation (SMPC), and differential privacy. By leveraging these approaches, AI-driven security frameworks can detect cyber threats, enforce access control, and mitigate privacy risks without compromising data confidentiality. Additionally, we examine the integration of zero-trust security models with AI-driven privacy mechanisms to strengthen cloud-edge ecosystems against emerging cyber threats.

Through a comparative analysis of traditional security methods versus AI-enhanced privacy models, we highlight the advantages of distributed learning architectures, encrypted inference techniques, and privacy-aware anomaly detection. Furthermore, real-world case studies from industry leaders (e.g., AWS, Microsoft Azure, Google Cloud) demonstrate how privacy-preserving AI is being implemented to protect sensitive workloads in cloud-edge environments.

Despite its transformative potential, implementing privacy-preserving AI presents challenges, including computational overhead, algorithmic bias, and adversarial vulnerabilities. We discuss ongoing research efforts and emerging trends such as decentralized AI, quantum-safe encryption, and edge-native privacy protocols to address these issues.

This paper concludes by providing recommendations for researchers, cloud service providers, and policymakers to drive the adoption of secure and privacy-aware AI models in cloud and edge computing. By advancing AI-driven privacy solutions, the industry can achieve a balance between security, performance, and compliance, ensuring a trusted and resilient digital ecosystem for the future.