Privacy-guardian : The Vital Need In Machine Learning With Big Data

This thesis studies the need for privacy in machine learning with big data. It proposes different frameworks and neural architectures to capture the sensitivities of user data, from which, the algorithms themselves decide how much they should learn from user data to protect their privacy while achieving good performances for downstream tasks.