MACHINE LEARNING ALGORITHMS FOR ENHANCING SECURITY AND PRIVACY IN CLOUD-BASED AI SYSTEMS

Abstract

The rapid growth of cloud computing and artificial intelligence (AI) has revolutionized the way organizations process and analyze data. However, the integration of AI systems in cloud environments has raised significant concerns regarding data security and privacy. This research article explores the application of machine learning algorithms to enhance the security and privacy of cloud-based AI systems. By leveraging advanced techniques such as homomorphic encryption, federated learning, and differential privacy, the proposed approaches aim to protect sensitive data, prevent unauthorized access, and ensure the confidentiality of AI models and results. The article presents a comprehensive analysis of existing security and privacy challenges in cloud-based AI systems, discusses the potential of machine learning algorithms in addressing these challenges, and proposes novel frameworks that integrate multiple security and privacy-preserving techniques. The research findings demonstrate the effectiveness of the proposed approaches in enhancing the security and privacy of cloud-based AI systems while maintaining high performance and utility.