Start with Privacy by Design in All Big Data Applications

Abstract

The term “Big Data” is used to describe a universe of very large datasets that hold a variety of data types. This has spawned a new generation of information architectures and applications to facilitate the fast processing speeds and the visualization needed to analyze and extract value from these extremely large sets of data, using distributed platforms. While not all data in Big Data applications will be personally identifiable, when this is the case, privacy interests arise. To be clear, privacy requirements are not obstacles to innovation or to realizing societal benefits from Big Data analytics—in fact, they can actually foster innovation and doubly-enabling, win–win outcomes. This is achieved by taking a Privacy by Design approach to Big Data applications. This chapter begins by defining information privacy, then it will provide an overview of the privacy risks associated with Big Data applications. Finally, the authors will discuss Privacy by Design as an international framework for privacy, then provide guidance on using the Privacy by Design Framework and the 7 Foundational Principles, to achieve both innovation and privacy—not one at the expense of the other.