TIENE EN SU CESTA DE LA COMPRA
en total 0,00 €
en total 0,00 €
Fundamentals of privacy, design for privacy and anonymization of multidimensional data.
Anonymization design for complex data structures like graph, time series, longitudinal and transaction data.
Threat models or the threats posed to sensitive data in an enterprise are discussed.
Applications of privacy: Privacy preserving data mining (briefly), Privacy preserving test data management (PPTDM), Synthetic data generation
Dynamic data protection using tokenization
Data Privacy Principles & PPTDM Manifesto
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.
Table of Contents
Introduction to Privacy; Static Data Anonymization Part I: Multidimensional Data; Static Data Anonymization Part II: Complex Data Structures; Static Data Anonymization Part III: Threats to Anonymized Data; Privacy Preserving Data Mining (PPDM); Privacy Preserving Test Data Manufacturing (PPTDM); Synthetic Data Generation; Dynamic Data Protection: Tokenization; Privacy Regulations; Appendix A: Anonymization Design Principles for Multidimensional Data; Appendix B: PPTDM Manifesto