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AbstractWith the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address thesedoi:10.1186/s42400-021-00092-8 pmid:34805760 pmcid:PMC8591683 fatcat:vmdemrhszjcynp6rytvckg7o5i