Privacy-Preserving Machine Learning Techniques for Securing Cloud Data

The increasing reliance on cloud computing necessitates robust data security measures to protect sensitive information. Privacy-preserving machine learning (PPML) has emerged as a critical area for ensuring data confidentiality while leveraging cloud-based analytics. Within this domain, our research focuses on enhancing PPML techniques to secure cloud data more effectively. Existing literature highlights significant challenges, including computational overhead, model accuracy, and scalability issues. Our novel approach integrates hybrid encryption, adaptive federated learning, and enhanced differential privacy to address these challenges. This is the first time such a comprehensive methodology has been proposed. Our results demonstrate significant improvements in computational efficiency, accuracy, and privacy preservation compared to existing methods, highlighting the potential of our framework to set new standards in cloud data security.