SEML: um processo para execução segura de aprendizagem de máquina em nuvem.

Abstract

Machine learning algorithms are crucial to the development of software used in our daily lives. In the face of the large amount of data and the growing need for computational data to train these algorithms, modern computing has migrated to the cloud. On the other hand, there are concerns regarding the security of the data that travel in this transmission medium. This work aims to develop a process that ensures the confidentiality of machine learning applications using reliable execution technologies.