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The book covers data privacy in depth with respect to data mining, test data management, synthetic data generation etc. It formalizes principles of data privacy that are essential for good anonymization design based on the data format and discipline. The principles outline best practices and reflect on the conflicting relationship between privacy and utility. From a practice standpoint, it provides practitioners and researchers with a definitive guide to approach anonymization of various data formats, including multidimensional, longitudinal, time-series, transaction, and graph data. In addition to helping CIOs protect confidential data, it also offers a guideline as to how this can be implemented for a wide range of data at the enterprise level.

Notice and consent approaches, being the most prevalent legal frameworks, have in recent years come under fire. I suggest they fail because they rest on a historical approach to privacy justice, whereby the justice of a particular state of affairs is a function of whether each transaction on the way was just. Instead, I make use of a background justice framing. Even where consent is present it is inadequate to secure the values at stake. When we only assess the fairness or freedom of individual information transactions, we fail to see the way many can undercut the very values we seek to secure by requiring consent for disclosures in the first place. I propose a patterned principle to regulate the distribution of individual control over privacy, and to set the background against which individual notice and consent can still play a role, albeit a limited one.