Chapman and Hall/CRC, 2017. — 232 p. — ISBN: 1498721044, 9781498721042, 9781315353760, 1315353768
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.
Introduction to data privacy
Static data anonymization part I: multidimensional data
Static data anonymization part II: complex data structures
Static data anonymization part III: threats to anonymiized data
Privacy preserving data mining
Privacy preserving test data manufacturing
Synthetic data generation
Dynamic data protection : tokenization
Privacy regulations