This Chapter presents a summary of the contributions of this monograph, as well as a discussion of some possible avenues for future work. The main theme of this monograph was centred on the issue of generating privacy preserving data in an efficient and reliable manner. We pointed out, in Chap. 1 , that while individual privacy decisions are subjective, to provide firms guarantees of privacy in the digital sphere, solutions must adopt a collective view. That is individual privacy decisions must be viewed in terms of their impact on the privacy of other users. As such, generating privacy preserving datasets to enable data analytics must account for a variety of considerations centered both on the privacy decisions of data owners (users) and on contextual considerations such as data formats, processing power, and data usability. Some examples of applications that can benefit from privacy preserving data include: privacy preserving machine learning tools, marketing analysis, recommendation models, healthcare applications, and digital learning platforms.