As companies store more and more sensitive data in larger amounts over longer periods of time, they are under ever increasing pressure to be transparent about what data they collect, how they consume it, and why they need to retain it. Big data privacy engineering is an emerging data governance discipline that aims to provide methodologies, tools, and techniques such that the engineered systems provide acceptable levels of privacy.

In this session, we will present the design principles and design patterns required in highly scalable privacy engineering systems and highlight how privacy engineering best practice can streamline data management processes.