The harvesting of large data sets and the use of analytics clearly implicate privacy concerns. The tasks of ensuring data security and protecting privacy become harder as information is multiplied and shared ever more widely around the world. Information regarding individuals’ health, location, electricity use, and online activity is exposed to scrutiny, raising concerns about profiling, discrimination, exclusion, and loss of control. Traditionally, organizations used various methods of de-identification (anonymization, pseudonymization, encryption, key-coding, data sharding) to distance data from real identities and allow analysis to proceed while at the same time containing privacy concerns. Over the past few years, however, computer scientists have repeatedly shown that even anonymized data can often be re-identified and attributed to specific individuals.