The success of ML algorithms depends heavily on how sensitive Big Data is gathered, filtered, transformed and stored. In healthcare, this data can be numerical (vital signs), CT images, categorical (gender, race), or even free text (doctor’s notes, surveys). Creating a global data-collection eco-system is vital, but at the same time presents a huge challenge. Once Big Data is gathered, the question of secure data storage arises. This session, with accompanying panel discussion, will review the most recent solutions offered by academia and business in collaboration with hospitals.