Digital Privacy and Security – serie

In this book, the authors highlight the latest research findings on the security and privacy of federated learning systems. The main attacks and counterattacks in this booming field are presented to readers in connection with inference, poisoning, generative adversarial networks, differential privacy, secure multi-party computation, homomorphic encryption, and shuffle, respectively.   The book offers an essential overview for researchers who are new to the field, while also equipping them to explore this “uncharted territory.” For each topic, the authors first present the key concepts, followed by the most important issues and solutions, with appropriate references for further reading. The book is self-contained, and all chapters can be read independently. It offers a valuable resource for master’s students, upper undergraduates, Ph.D. students, and practicing engineers alike.

In this book, the authors highlight the latest research findings on the security and privacy of federated learning systems. The main attacks and counterattacks in this booming field are presented to readers in connection with inference, poisoning, generative adversarial networks, differential privacy, secure multi-party computation, homomorphic encryption, and shuffle, respectively.   The book offers an essential overview for researchers who are new to the field, while also equipping them to explore this “uncharted territory.” For each topic, the authors first present the key concepts, followed by the most important issues and solutions, with appropriate references for further reading. The book is self-contained, and all chapters can be read independently. It offers a valuable resource for master’s students, upper undergraduates, Ph.D. students, and practicing engineers alike.

As "right to be forgotten" style regulations, data governance requirements, and security concerns expand worldwide, researchers and practitioners need methods that go beyond ad hoc retraining and provide effective deletion from models. Machine unlearning has emerged as a core capability for trustworthy artificial intelligence (AI), enabling trained models to remove the influence of specific data after deployment. This book offers a systematic, end to end guide to machine unlearning, from foundational problem formulations to practical design patterns for real world systems. It introduces the unlearning paradigm and key evaluation criteria, then presents a structured treatment of exact unlearning and approximate unlearning, highlighting when each is appropriate and what trade-offs arise in utility, efficiency, and reliability. A dedicated section on unlearning auditing and verification explains how to test and validate deletion claims, including protocol level schemes, model centric auditing approaches, and benchmark driven stress testing at scale. The book then extends unlearning to domain specific settings, covering graph unlearning, federated unlearning, and emerging techniques for large language models and diffusion models. Finally, it examines privacy and security risks such as leakage, backdoors, and poisoning, and surveys defenses and future directions for building dependable unlearning services. Written for graduate students, researchers, and engineers, the book provides a coherent taxonomy, practical insights, and a roadmap for developing, evaluating, and deploying unlearning in modern AI pipelines.

This book explores the cutting-edge concept of machine unlearning and its application across various fields, especially within AI and machine learning models. It addresses the critical need to "forget" specific data in models to comply with evolving privacy regulations, enhance model robustness, and mitigate security risks. With a focus on real-world implications, this book presents a thorough analysis of unlearning techniques and frameworks, detailing approaches from exact data removal to approximate, efficient methods that support high-performance models in dynamic environments.The chapters delve into machine unlearning for large language models, addressing privacy concerns in unstructured data, and the challenges of catastrophic recalling. Each chapter provides readers with actionable insights into the mechanisms, benefits, and trade-offs involved in implementing unlearning. Readers will discover pioneering frameworks, such as federated fuzzy unlearning, and advanced techniques that combat over-unlearning, ensuring model integrity without extensive retraining.This book is designed for researchers, AI practitioners, and data scientists interested in integrating unlearning for ethical, secure, and adaptive AI systems. A foundational knowledge in AI or machine learning is recommended. By the end, readers will gain a robust understanding of unlearning methodologies and practical strategies to implement them within various applications, driving responsible AI innovation.

This book presents a comprehensive, research-driven framework for implementing cost-effective cybersecurity solutions through the integration of open-source security tools within a structured, multi-tiered defense model. Addressing the growing challenge of cybersecurity affordability, particularly for SMEs, educational institutions, and resource-constrained organizations, it offers a systematic approach to mitigating threats while ensuring scalability and resilience. The book provides empirical evaluations of security tools, real-world deployment insights, and practical implementation strategies, bridging the gap between theoretical cybersecurity principles and actionable solutions. By incorporating technical, economic, and strategic perspectives, it equips cybersecurity professionals, IT administrators, researchers, and policymakers with the knowledge to enhance security postures without incurring prohibitive costs. This publication stands out by offering a structured defense model, performance benchmarking of open-source solutions, and a forward-looking analysis of emerging threats, making it an essential resource for advancing cybersecurity resilience in financially constrained environments.

This book offers a comprehensive overview of Crypto-Forensics, covering both fundamental concepts and the latest trends and challenges. Written in clear and accessible language, it is designed to be understood by a wide range of readers. As digital media usage continues to grow, so does the importance of Crypto-Forensics. With more individuals and organizations relying on multimedia content for communication, education, entertainment, and commerce, it is critical to understand the security risks associated with multimedia content and the techniques to mitigate those risks. This book aims to provide that understanding and serve as a valuable resource for anyone interested in Crypto-Forensics.

Web 3.0 is the next generation of the Internet built on decentralized technologies such as blockchain and cryptography. It is designed to address issues encountered in the previous generation of the Internet such as imbalanced distribution of interests, monopoly of platform resources, and leakage of personal privacy. This book explores the challenges and solutions related to ensuring security and privacy in the context of the evolving Web 3.0 architecture.Web 3.0 represents the next generation of the web, characterized by decentralized networks, blockchain technology, and enhanced user control over data. As Web 3.0 evolves, the focus on addressing security and privacy concerns becomes increasingly crucial. This book provides a comprehensive understanding of the security and privacy issues specific to Web 3.0 and offers practical defense frameworks and methods to mitigate these challenges.This book is dedicated to the specific aspects of security and privacy in Web 3.0, from introducing the architecture and addressing the inherent issues to presenting innovative methods for privacy-preserving computing, user behavior identification, and abnormal transaction detection. It is of particular interest to researchers in the field of Web 3.0, blockchain, and network security, as it summarizes the security and privacy concerns in Web 3.0 and brings a number of innovative technologies and practical solutions to protect sensitive data and maintain user privacy in Web 3.0 environments, equipping researchers with the knowledge necessary to design secure and privacy-aware applications and systems in Web 3.0.