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Full Description
V1 - The book uses practical lessons learned in applying real-life concepts and tools to help security leaders and their teams craft and implement strategies. A common goal is to find the right balance between compliance, privacy requirements, and the business utility of data.
V2 - The book will review how new and old privacy-preserving techniques can provide practical protection for data in transit, use, and rest. It will use practical lessons in Data Integrity, and Trust, and data's business utility. It is based on a good understanding and experience of new and old technologies, emerging trends, and a broad experience from many projects in this domain.
Contents
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.
Volume 2
Section 1. Vision and Best Practices. 1. Risks and Threats. 2. Opportunities and Innovation. 3. Best Practices. 4. Vision and Roadmap. Section 2. Trust and Hybrid Cloud. 5. Zero Trust and Zero-knowledge proofs. 6. Data Protection for Hybrid Cloud. 7. Web 3.0 and Data Security. Section 3. Data Quality. 8. Metadata and Provenance of Data. 9. Data Security and Quality. 10. Analytics, Data Lakes, and Federated learning. Summary. Glossary. Appendices. A. The Future of Encryption. B. Synthetic Data and differential privacy. C. API Security. D. Blockchain Security. E. Data Governance Tools.
