This book provides a complete reference for personal data identification, classification, anonymization, and usage in various application contexts.
Autorentext
Nataraj Venkataramanan is currently Associate Vice President at HCL Technologies Ltd, India. He has over two decades of experience in Computing and has worked previously in some of India's Information Technology (IT) majors. He has worked across different domains like Banking, Financial Services, Insurance, Government, Oil & Gas, Retail and Manufacturing. His main research interests are in Large Scale Software Architecture, Quality Attributes of Software Architecture, Data Privacy, Privacy Preserving Data Mining, Data Analytics, Pattern Recognition and Learning Systems. He has published refereed technical papers in journals and conferences. He is a member of IEEE and ACM. He can be reached at nataraj.venkataramanan@gmail.com
Ashwin Shriram works for HCL Technologies as a Solution Architect. An engineer in Computer Science, he comes from a strong technical background in Data Management. At HCL, Ashwin is a senior member of the Test Data Management Center of Excellence. His current research interests include Data Privacy, Data Analytics, Pattern Recognition and Big Data Privacy. Prior to joining HCL, Ashwin was working in USA for customers in public as well as private sectors. He can be reached at ashwin.shriram@gmail.com.
Zusammenfassung
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.
Inhalt
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