DESCRIPTION In an age where data breaches and privacy issues are growing, confidential computing offers a state-of-the-art solution. This technology helps organizations keep their most sensitive information safe, even against strong threats. Introduction to Confidential Computing provides a clear guide to this advanced cybersecurity technology. It explains why confidential computing is crucial for protecting sensitive data, covering key technologies, architectures, and frameworks. The book details remote attestation, which ensures computing environment integrity, and explores how confidential computing enhances AI training security. It also reviews solutions from major cloud providers, helping readers choose the right options. This book is perfect for anyone looking to boost their cybersecurity skills and stay ahead in data protection. By the end of this book, you will gain a thorough understanding of confidential computing and its impact on data security and privacy. You will be ready to assess and implement confidential computing solutions, safeguarding your organization's assets and meeting data privacy regulations. KEY FEATURES ● Comprehensive overview of confidential computing architectures and technologies. ● Guidance on leveraging confidential computing technologies for secure data processing and privacy protection. ● Learn how confidential computing ensures data privacy and protects AI model integrity through secure processing. WHAT YOU WILL LEARN ● Learn why confidential computing is crucial in today's digital world. ● Understand high-level architecture of various trusted execution environments implementations. ● Art of developing secure applications that can be deployed on TEE. ● Comprehend how remote attestation ensures the integrity of computing environments. ● Discover how RA-TLS could reshape the future of secure communications. ● Explore how confidential computing protects data and AI models. WHO THIS BOOK IS FOR This book is for software architects, security researchers, and developers to enhance application security using confidential computing. PhD candidates and postgraduates will explore TEEs, while AI/ML developers will understand how confidential AI protects data and models. TABLE OF CONTENTS 1. Vital Need for Confidential Computing 2. Trusted Execution Environments 3. Secure Application Development 4. Remote Attestation 5. Confidential Computing in Cloud 6. Confidential Artificial Intelligence 7. Prospects of Confidential Computing Appendix A: Enclave Initialization Instructions in Intel SGX Appendix B: Intel TDX Architectural Instructions Appendix C: Secure Boot Infrastructure Terminologies

This book highlights the three pillars of data security, viz protecting data at rest, in transit, and in use. Protecting data at rest means using methods such as encryption or tokenization so that even if data is copied from a server or database, a thief cannot access the information. Protecting data in transit means making sure unauthorized parties cannot see information as it moves between servers and applications. There are well-established ways to provide both kinds of protection. Protecting data while in use, though, is especially tough because applications need to have data in the clear—not encrypted or otherwise protected—in order to compute. But that means malware can dump the contents of memory to steal information. It does not really matter if the data was encrypted on a server’s hard drive if it is stolen while exposed in memory.​ As computing moves to span multiple environments—from on-premise to public cloud to edge—organizations need protection controls that help safeguard sensitive IP and workload data wherever the data resides. Many organizations have declined to migrate some of their most sensitive applications to the cloud because of concerns about potential data exposure. Confidential computing makes it possible for different organizations to combine data sets for analysis without accessing each other’s data.

Master the art of designing, developing, and operating secure infrastructures on Google Cloud Key Features Prepare for the certification exam with clear explanations, real-world examples, and self-assessment questions Review Google Cloud security best practices for building a secure and compliant cloud environment Explore advanced concepts like Security Command Center, BeyondCorp Zero Trust, and container security Book DescriptionGoogle Cloud security offers powerful controls to assist organizations in establishing secure and compliant cloud environments. With this book, you’ll gain in-depth knowledge of the Professional Cloud Security Engineer certification exam objectives, including Google Cloud security best practices, identity and access management (IAM), network security, data security, and security operations. The chapters go beyond the exam essentials, helping you explore advanced topics such as Google Cloud Security Command Center, the BeyondCorp Zero Trust architecture, and container security. With step-by-step explanations, practical examples, and practice exams to help you improve your skills for the exam, you'll be able to efficiently review and apply key concepts of the shared security responsibility model. Finally, you’ll get to grips with securing access, organizing cloud resources, network and data security, and logging and monitoring. By the end of this book, you'll be proficient in designing, developing, and operating security controls on Google Cloud and gain insights into emerging concepts for future exams.What you will learn Understand how Google secures infrastructure with shared responsibility Use resource hierarchy for access segregation and implementing policies Utilize Google Cloud Identity for authentication and authorizations Build secure networks with advanced network features Encrypt/decrypt data using Cloud KMS and secure sensitive data Gain visibility and extend security with Google's logging and monitoring capabilities Who this book is forThis book is for IT professionals, cybersecurity specialists, system administrators, and tech enthusiasts aspiring to strengthen their understanding of Google Cloud security and elevate their career trajectory. Earning this certification not only validates your expertise but also makes you part of an elite group of GCP security engineers, opening doors to opportunities that can significantly advance your career. Prior knowledge of the foundational concepts of Google Cloud or GCP Associate Engineer Certification is strongly recommended.

Plan, build, and maintain highly secure Azure applications and workloads As business-critical applications and workloads move to the Microsoft Azure cloud, they must stand up against dangerous new threats. That means you must build robust security into your designs, use proven best practices across the entire development lifecycle, and combine multiple Azure services to optimize security. Now, a team of leading Azure security experts shows how to do just that. Drawing on extensive experience securing Azure workloads, the authors present a practical tutorial for addressing immediate security challenges, and a definitive design reference to rely on for years. Learn how to make the most of the platform by integrating multiple Azure security technologies at the application and network layers— taking you from design and development to testing, deployment, governance, and compliance. About You This book is for all Azure application designers, architects, developers, development managers, testers, and everyone who wants to make sure their cloud designs and code are as secure as possible. Discover powerful new ways to: Improve app / workload security, reduce attack surfaces, and implement zero trust in cloud code Apply security patterns to solve common problems more easily Model threats early, to plan effective mitigations Implement modern identity solutions with OpenID Connect and OAuth2 Make the most of Azure monitoring, logging, and Kusto queries Safeguard workloads with Azure Security Benchmark (ASB) best practices Review secure coding principles, write defensive code, fix insecure code, and test code security Leverage Azure cryptography and confidential computing technologies Understand compliance and risk programs Secure CI / CD automated workflows and pipelines Strengthen container and network security

Gain hands-on experience in data privacy and privacy-preserving machine learning with open-source ML frameworks, while exploring techniques and algorithms to protect sensitive data from privacy breaches Key Features Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches Develop and deploy privacy-preserving ML pipelines using open-source frameworks Gain insights into confidential computing and its role in countering memory-based data attacks Purchase of the print or Kindle book includes a free PDF eBook Book Description– In an era of evolving privacy regulations, compliance is mandatory for every enterprise – Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information – This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases – As you progress, you’ll be guided through developing anti-money laundering solutions using federated learning and differential privacy – Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models – You’ll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field – Upon completion, you’ll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks What you will learn Study data privacy, threats, and attacks across different machine learning phases Explore Uber and Apple cases for applying differential privacy and enhancing data security Discover IID and non-IID data sets as well as data categories Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks Understand secure multiparty computation with PSI for large data Get up to speed with confidential computation and find out how it helps data in memory attacks Who this book is for – This comprehensive guide is for data scientists, machine learning engineers, and privacy engineers – Prerequisites include a working knowledge of mathematics and basic familiarity with at least one ML framework (TensorFlow, PyTorch, or scikit-learn) – Practical examples will help you elevate your expertise in privacy-preserving machine learning techniques

This book deeply analyzes the theoretical roots of the development of global artificial intelligence ethics and AI governance, the ethical issues in AI application scenarios, and the discussion of artificial intelligence governance issues from a global perspective. From the perspective of knowledge, the book includes not only the metaphysical research of traditional Western ethics, but also the interpretation of AI-related practical cases and international policies. The purpose of this book is not only to study AI ethics and governance issues academically, but to seek a path to solve problems in the real world. It is a very meaningful monograph in both academic theory and reality. This book responds to the implementation of China's digital economy governance and other topics. It is a cutting-edge academic monograph that combines industry, policy, and thought. In this book, the author not only discusses the humanities thoughts such as ethics, political economy, philosophy, and sociology, but also involves computer science, biology, and medicine and other science and engineering disciplines, effectively using interdisciplinary thinking as readers clarify how to explore ethical consensus and establish smart social governance rules in the era of artificial intelligence, so as to provide the most comprehensive and unique scientific and technological insights for smart economy participants, related practitioners in the artificial intelligence industry, and government policy makers. For academia, this is a representative book of Chinese scholars' systematic thinking on AI ethical propositions from a global perspective. For the industry, this is a book that understands the policies and ethical propositions faced by the development of AI industry. An important reference book, for policy makers, this is a monograph for understanding how policies in the AI industry make decisions that conform to AI industry practices and people's moral order.

Learn to analyze and measure risk by exploring the nature of trust and its application to cybersecurity Trust in Computer Systems and the Cloud delivers an insightful and practical new take on what it means to trust in the context of computer and network security and the impact on the emerging field of Confidential Computing. Author Mike Bursell’s experience, ranging from Chief Security Architect at Red Hat to CEO at a Confidential Computing start-up grounds the reader in fundamental concepts of trust and related ideas before discussing the more sophisticated applications of these concepts to various areas in computing. The book demonstrates in the importance of understanding and quantifying risk and draws on the social and computer sciences to explain hardware and software security, complex systems, and open source communities. It takes a detailed look at the impact of Confidential Computing on security, trust and risk and also describes the emerging concept of trust domains, which provide an alternative to standard layered security. Foundational definitions of trust from sociology and other social sciences, how they evolved, and what modern concepts of trust mean to computer professionals A comprehensive examination of the importance of systems, from open-source communities to HSMs, TPMs, and Confidential Computing with TEEs. A thorough exploration of trust domains, including explorations of communities of practice, the centralization of control and policies, and monitoring Perfect for security architects at the CISSP level or higher, Trust in Computer Systems and the Cloud is also an indispensable addition to the libraries of system architects, security system engineers, and master’s students in software architecture and security.