comprehensive security and privacy framework against malicious insider in cloud-based machine learning

Cloud-based machine learning has become an increasingly popular approach for training and deploying machine learning models, thanks to its scalability, cost-effectiveness, and ease of access. however, the use of cloud-based machine learning also introduces new security and privacy challenges, particularly with respect to insider threats. in this proposed research project, we aim to develop a multi-faceted approach to enhancing security and privacy in cloud-based machine learning. our approach will draw on a range of techniques, including fully homomorphic encryption, multi-factor authentication. the proposed framework conducts a comprehensive evaluation using a variety of datasets and use cases, and this approach provides higher security and privacy as compared to existing security and privacy frameworks for cloud-based machine learning. the ultimate goal is to provide practical and effective solutions for enhancing security and privacy in cloud-based machine learning, and to contribute to the ongoing efforts to address the challenges of insider threats in this rapidly evolving field.