This is an open access title available under the terms of a CC BY-NC-ND 4.0 License. It is free to read, download and share on Elgaronline.com. The Dictionary of Privacy, Data Protection and Information Security explains the complex technical terms, legal concepts, privacy management techniques, conceptual matters and vocabulary that inform public debate about privacy.
This glossary provides a central resource of definitions most commonly used in Nat. Institute of Standards and Technology (NIST) information security publications and in the Committee for National Security Systems (CNSS) information assurance publications. Each entry in the glossary points to one or more source NIST publications, and/or CNSSI-4009, and/or supplemental sources where appropriate. This is a print on demand edition of an important, hard-to-find publication.
The use of information networks for business and government is expanding enormously. Government use of networks features prominently in plans to make government more efficient, effective, and responsive. But the transformation brought about by the networking also raises new concerns for the security and privacy of networked information. This Office of Technology Assessment (OTA) report was requested by the Senate Committee on Governmental Affairs and the House Subcommittee on Telecommunications and Finance. The report begins with background information and an overview of the current situation, a statement of the problems involved in safeguarding unclassified networked information, and a summary of policy issues and options. The major part of the report is then devoted to detailed discussions of policy issues in three areas: (1) cryptography policy, including federal information processing standards and export controls; (2) guidance on safeguarding unclassified information in federal agencies; and (3) legal issues and information security, including electronic commerce, privacy, and intellectual property. Appendices include Congressional letters of request; the Computer Security Act and related documents; evolution of the digital signature standard; and lists of workshop participants, reviews, and other contributors. An index is provided. A separately published eight-page OTA Report Summary is included. (JLB).
Business intelligence supports managers in enterprises to make informed business decisions in various levels and domains such as in healthcare. These technologies can handle large structured and unstructured data (big data) in the healthcare industry. Because of the complex nature of healthcare data and the significant impact of healthcare data analysis, it is important to understand both the theories and practices of business intelligence in healthcare. Theory and Practice of Business Intelligence in Healthcare is a collection of innovative research that introduces data mining, modeling, and analytic techniques to health and healthcare data; articulates the value of big volumes of data to health and healthcare; evaluates business intelligence tools; and explores business intelligence use and applications in healthcare. While highlighting topics including digital health, operations intelligence, and patient empowerment, this book is ideally designed for healthcare professionals, IT consultants, hospital directors, data management staff, data analysts, hospital administrators, executives, managers, academicians, students, and researchers seeking current research on the digitization of health records and health systems integration.
Companies, lawyers, privacy officers, compliance managers, as well as human resources, marketing and IT professionals are increasingly facing privacy issues. While plenty of information is freely available, it can be difficult to grasp a problem quickly, without getting lost in details and advocacy. This is where Determann’s Field Guide to Data Privacy Law comes into its own – identifying key issues and providing concise practical guidance for an increasingly complex field shaped by rapid change in international laws, technology and society
Providing comparative analysis that examines both Western and non-Western legal systems, this wide-ranging Handbook expands and enriches the existing privacy and defamation law literature and addresses the fundamental issues facing today’s scholars and practitioners. Comparative Privacy and Defamation provides insightful commentary on issues of theory and doctrine, including the challenges of General Data Protection Regulations (GDPR) and the impact of new technologies on the law.
Presenting a concise, yet wide-ranging and contemporary overview of the field, this Advanced Introduction to Privacy Law focuses on how we arrived at our privacy laws, and how the law can deal with new and emerging challenges from digital technologies, social networks and public health crises. This illuminating and interdisciplinary book demonstrates how the history of privacy law has been one of constant adaptation to emerging challenges, illustrating the primacy of the right to privacy amidst a changing social and cultural landscape.
The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.
This textbook presents a proven, mature Model-Based Systems Engineering (MBSE) methodology that has delivered success in a wide range of system and enterprise programs. The authors introduce MBSE as the state of the practice in the vital Systems Engineering discipline that manages complexity and integrates technologies and design approaches to achieve effective, affordable, and balanced system solutions to the needs of a customer organization and its personnel. The book begins with a summary of the background and nature of MBSE. It summarizes the theory behind Object-Oriented Design applied to complex system architectures. It then walks through the phases of the MBSE methodology, using system examples to illustrate key points. Subsequent chapters broaden the application of MBSE in Service-Oriented Architectures (SOA), real-time systems, cybersecurity, networked enterprises, system simulations, and prototyping. The vital subject of system and architecture governance completes the discussion. The book features exercises at the end of each chapter intended to help readers/students focus on key points, as well as extensive appendices that furnish additional detail in particular areas. The self-contained text is ideal for students in a range of courses in systems architecture and MBSE as well as for practitioners seeking a highly practical presentation of MBSE principles and techniques.
