This book uses practical lessons learned in applying real-life concepts and tools to help security leaders and their teams craft and implement strategies. These projects deal with a variety of use cases and data types.
This book uses practical lessons learned in applying real-life concepts and tools to help security leaders and their teams craft and implement strategies. These projects deal with a variety of use cases and data types.
Ulf Mattsson is a recognized information security and data privacy expert with a strong track record of more than two decades implementing cost-effective data security and privacy controls for global Fortune 500 institutions, including Citigroup, Goldman Sachs, GE Capital, BNY Mellon, AIG, Visa USA, Mastercard Worldwide, American Express, The Coca Cola Company, Wal-Mart, BestBuy, KOHL's, Microsoft, IBM, Informix, Sybase, Teradata, and RSA Security. He is currently the Chief Security Strategist and earlier the Chief Technology Officer at Protegrity, a data security company he co-founded after working 20 years at IBM in software development. Ulf is an inventor of more than 70 issued US patents in data privacy and security. Ulf is active in the information security industry as a contributor to the development of data privacy and security standards in the Payment Card Industry Data Security Standard (PCI DSS) and American National Standards Institute (ANSI) X9 for financial industry. He is on the advisory board of directors at PACE University, NY, in the area of cloud security and a frequent speaker at various international events and conferences, including the RSA Conference, and the author of more than 100 in-depth professional articles and papers on data privacy and security, including IBM Journals, IEEE Xplore, ISSA Journal and ISACA Journal. Ulf also holds a master's in physics in Engineering from Chalmers University of Technology in Sweden and is Co-Author of Defending the database (Elsevier Ltd, 2007) and Real security for virtual machines (Elsevier Ltd, 2009)
Inhaltsangabe
Controlling Privacy and the Use of Data Assets - Volume 1 Introduction, Acknowledgments. About the Author. SECTION I Introduction and Vision. Chapter 1 Privacy, Risks, and Threats. Chapter 2 Trends and Evolution. Chapter 3 Best Practices, Roadmap, and Vision. SECTION II Data Confidentiality and Integrity. Chapter 4 Computing on Encrypted Data. Chapter 5 Reversible Data Protection Techniques. Chapter 6 Non-Reversible Data Protection Techniques. SECTION III Users and Authorization. Chapter 7 Access Control. Chapter 8 Zero Trust Architecture. SECTION IV Applications . Chapter 9 Applications, APIs, and Privacy by Design. Chapter 10 Machine Learning and Analytics. Chapter 11 Secure Multiparty Computing. Chapter 12 Encryption and Tokenization of International Unicode Data. Chapter 13 Blockchain and Data Lineage. SECTION V Platforms. Chapter 14 Hybrid Cloud, CASB, and SASE. Chapter 15 HSM, TPM, and Trusted Execution Environments. Chapter 16 Internet of Things. Chapter 17 Quantum Computing. Chapter 18 Summary. Appendix A Standards and Regulations. Appendix B Governance, Guidance, and Frameworks. Appendix C Data Discovery and Search. Appendix D Digital Commerce, Gamification, and AI. Appendix E Innovation and Products. Appendix F Glossary. Index. Controlling Privacy and the Use of Data Assets - Volume 2 Foreword - Ben Rothke, CISSP, CISM, Senior Information Security Manager, Tapad, Inc. New York, NY. Foreword - Jim Ambrosini, CISA, CRISC, CISSP Cybersecurity Consultant and CISO. Foreword - Richard Purcell, CEO, Corporate Privacy Group (former Chief Privacy. Officer, Microsoft). Acknowledgments. About the Author. Introduction. SECTION I Vision and Best Practices. Chapter 1 Risks and Threats. Chapter 2 Opportunities. Chapter 3 Best Practices. Chapter 4 Vision and Roadmap. SECTION II Trust and Hybrid Cloud. Chapter 5 Zero Trust and Hybrid Cloud. Chapter 6 Data Protection for Hybrid Cloud. Chapter 7 Web 3.0 and Data Security. SECTION III Data Quality. Chapter 8 Metadata and the Provenance of Data. Chapter 9 Data Security and Quality. Chapter 10 Analytics, Data Lakes, and Federated Learning. Chapter 11 Summary. Glossary. Appendix A: The 2030 Environment. Appendix B: Synthetic Data and Differential Privacy. Appendix C: API Security. Appendix D: Blockchain Architecture and Zero-Knowledge Proof. Appendix E: Data Governance Tools. Index.
Controlling Privacy and the Use of Data Assets - Volume 1 Introduction, Acknowledgments. About the Author. SECTION I Introduction and Vision. Chapter 1 Privacy, Risks, and Threats. Chapter 2 Trends and Evolution. Chapter 3 Best Practices, Roadmap, and Vision. SECTION II Data Confidentiality and Integrity. Chapter 4 Computing on Encrypted Data. Chapter 5 Reversible Data Protection Techniques. Chapter 6 Non-Reversible Data Protection Techniques. SECTION III Users and Authorization. Chapter 7 Access Control. Chapter 8 Zero Trust Architecture. SECTION IV Applications . Chapter 9 Applications, APIs, and Privacy by Design. Chapter 10 Machine Learning and Analytics. Chapter 11 Secure Multiparty Computing. Chapter 12 Encryption and Tokenization of International Unicode Data. Chapter 13 Blockchain and Data Lineage. SECTION V Platforms. Chapter 14 Hybrid Cloud, CASB, and SASE. Chapter 15 HSM, TPM, and Trusted Execution Environments. Chapter 16 Internet of Things. Chapter 17 Quantum Computing. Chapter 18 Summary. Appendix A Standards and Regulations. Appendix B Governance, Guidance, and Frameworks. Appendix C Data Discovery and Search. Appendix D Digital Commerce, Gamification, and AI. Appendix E Innovation and Products. Appendix F Glossary. Index. Controlling Privacy and the Use of Data Assets - Volume 2 Foreword - Ben Rothke, CISSP, CISM, Senior Information Security Manager, Tapad, Inc. New York, NY. Foreword - Jim Ambrosini, CISA, CRISC, CISSP Cybersecurity Consultant and CISO. Foreword - Richard Purcell, CEO, Corporate Privacy Group (former Chief Privacy. Officer, Microsoft). Acknowledgments. About the Author. Introduction. SECTION I Vision and Best Practices. Chapter 1 Risks and Threats. Chapter 2 Opportunities. Chapter 3 Best Practices. Chapter 4 Vision and Roadmap. SECTION II Trust and Hybrid Cloud. Chapter 5 Zero Trust and Hybrid Cloud. Chapter 6 Data Protection for Hybrid Cloud. Chapter 7 Web 3.0 and Data Security. SECTION III Data Quality. Chapter 8 Metadata and the Provenance of Data. Chapter 9 Data Security and Quality. Chapter 10 Analytics, Data Lakes, and Federated Learning. Chapter 11 Summary. Glossary. Appendix A: The 2030 Environment. Appendix B: Synthetic Data and Differential Privacy. Appendix C: API Security. Appendix D: Blockchain Architecture and Zero-Knowledge Proof. Appendix E: Data Governance Tools. Index.
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