This book summarizes recent inventions, provides guidelines and recommendations, and demonstrates many practical applications of homomorphic encryption. This collection of papers represents the combined wisdom of the community of leading experts on Homomorphic Encryption. In the past 3 years, a global community consisting of researchers in academia, industry, and government, has been working closely to standardize homomorphic encryption. This is the first publication of whitepapers created by these experts that comprehensively describes the scientific inventions, presents a concrete security analysis, and broadly discusses applicable use scenarios and markets. This book also features a collection of privacy-preserving machine learning applications powered by homomorphic encryption designed by groups of top graduate students worldwide at the Private AI Bootcamp hosted by Microsoft Research. The volume aims to connect non-expert readers with this important new cryptographic technology in an accessible and actionable way. Readers who have heard good things about homomorphic encryption but are not familiar with the details will find this book full of inspiration. Readers who have preconceived biases based on out-of-date knowledge will see the recent progress made by industrial and academic pioneers on optimizing and standardizing this technology. A clear picture of how homomorphic encryption works, how to use it to solve real-world problems, and how to efficiently strengthen privacy protection, will naturally become clear.

This book explores the latest developments in fully homomorphic encryption (FHE), an effective means of performing arbitrary operations on encrypted data before storing it in the ‘cloud’. The book begins by addressing perennial problems like sorting and searching through FHE data, followed by a detailed discussion of the basic components of any algorithm and adapting them to handle FHE data. In turn, the book focuses on algorithms in both non-recursive and recursive versions and discusses their realizations and challenges while operating in the FHE domain on existing unencrypted processors. It highlights potential complications and proposes solutions for encrypted database design with complex queries, including the basic design details of an encrypted processor architecture to support FHE operations in real-world applications.

This is a graduate textbook of advanced tutorials on the theory of cryptography and computational complexity. In particular, the chapters explain aspects of garbled circuits, public-key cryptography, pseudorandom functions, one-way functions, homomorphic encryption, the simulation proof technique, and the complexity of differential privacy. Most chapters progress methodically through motivations, foundations, definitions, major results, issues surrounding feasibility, surveys of recent developments, and suggestions for further study. This book honors Professor Oded Goldreich, a pioneering scientist, educator, and mentor. Oded was instrumental in laying down the foundations of cryptography, and he inspired the contributing authors, Benny Applebaum, Boaz Barak, Andrej Bogdanov, Iftach Haitner, Shai Halevi, Yehuda Lindell, Alon Rosen, and Salil Vadhan, themselves leading researchers on the theory of cryptography and computational complexity. The book is appropriate for graduate tutorials and seminars, and for self-study by experienced researchers, assuming prior knowledge of the theory of cryptography.

This open access book provides the first comprehensive collection of papers that provide an integrative view on cybersecurity. It discusses theories, problems and solutions on the relevant ethical issues involved. This work is sorely needed in a world where cybersecurity has become indispensable to protect trust and confidence in the digital infrastructure whilst respecting fundamental values like equality, fairness, freedom, or privacy. The book has a strong practical focus as it includes case studies outlining ethical issues in cybersecurity and presenting guidelines and other measures to tackle those issues. It is thus not only relevant for academics but also for practitioners in cybersecurity such as providers of security software, governmental CERTs or Chief Security Officers in companies.

Protecting Privacy in Video Surveillance offers the state of the art from leading researchers and experts in the field. This broad ranging volume discusses the topic from various technical points of view and also examines surveillance from a societal perspective. A comprehensive introduction carefully guides the reader through the collection of cutting-edge research and current thinking. The technical elements of the field feature topics from MERL blind vision, stealth vision and privacy by de-identifying face images, to using mobile communications to assert privacy from video surveillance, and using wearable computing devices for data collection in surveillance environments. Surveillance and society is approached with discussions of security versus privacy, the rise of surveillance, and focusing on social control. This rich array of the current research in the field will be an invaluable reference for researchers, as well as graduate students.

This book provides a collection of papers from the Ninth Workshop on Computing: Theory and Practice, WCTP 2019 devoted to theoretical and practical approaches to computation, which was organized by four top universities in Japan and the Philippines: Tokyo Institute of Technology, Osaka University, the University of the Philippines Diliman, and De La Salle University. The proceedings provide a broad overview of recent research trends in computer science research in Asia, particularly in these two countries. The papers included in the proceedings focus on both theoretical and practical aspects of computations, such as programming language theory, modeling of software systems, applications of machine learning, empathic computing, and various applications of information technology.

This book constitutes the refereed proceedings of the 8th International Conference on Grid and Pervasive Computing, GPC 2013, held in Seoul, Korea, in May 2013 and the following colocated workshops: International Workshop on Ubiquitous and Multimedia Application Systems, UMAS 2013; International Workshop DATICS-GPC 2013: Design, Analysis and Tools for Integrated Circuits and Systems; and International Workshop on Future Science Technologies and Applications, FSTA 2013. The 111 revised papers were carefully reviewed and selected from numerous submissions. They have been organized in the following topical sections: cloud, cluster and grid; middleware resource management; mobile peer-to-peer and pervasive computing; multi-core and high-performance computing; parallel and distributed systems; security and privacy; ubiquitous communications, sensor networking, and RFID; ubiquitous and multimedia application systems; design, analysis and tools for integrated circuits and systems; future science technologies and applications; and green and human information technology.

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi’an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.

As cyber threats become increasingly sophisticated, the need for innovative defense strategies becomes urgent. Generative artificial intelligence (AI) offers a revolutionary approach to enhance cybersecurity. By utilizing advanced algorithms, data analysis, and machine learning, generative AI can simulate complex attack scenarios, identify vulnerabilities, and develop proactive defense mechanisms while adapting to modern-day cyber-attacks. AI strengthens current organizational security while offering quick, effective responses to emerging threats. Decisive strategies are needed to integrate generative AI into businesses defense strategies and protect organizations from attacks, secure digital data, and ensure safe business processes. Utilizing Generative AI for Cyber Defense Strategies explores the utilization of generative AI tools in organizational cyber security and defense. Strategies for effective threat detection and mitigation are presented, with an emphasis on deep learning, artificial intelligence, and Internet of Things (IoT) technology. This book covers topics such as cyber security, threat intelligence, and behavior analysis, and is a useful resource for computer engineers, security professionals, business owners, government officials, data analysts, academicians, scientists, and researchers.