Massive amounts of data on human beings can now be analyzed. Pragmatic purposes abound, including selling goods and services, winning political campaigns, and identifying possible terrorists. Yet 'big data' can also be harnessed to serve the public good: scientists can use big data to do research that improves the lives of human beings, improves government services, and reduces taxpayer costs. In order to achieve this goal, researchers must have access to this data - raising important privacy questions. What are the ethical and legal requirements? What are the rules of engagement? What are the best ways to provide access while also protecting confidentiality? Are there reasonable mechanisms to compensate citizens for privacy loss? The goal of this book is to answer some of these questions. The book's authors paint an intellectual landscape that includes legal, economic, and statistical frameworks. The authors also identify new practical approaches that simultaneously maximize the utility of data access while minimizing information risk.

This encyclopedia is the most comprehensive and up-to-date source of reference for sustainability in business and management. It covers both traditional and emerging concepts and terms and is fully international in its scope. More than 700 contributions of internationally renowned experts provide a definitive access to the knowledge in the area of sustainable and responsible management. All actors in the field will find reliable and up to date definitions and explanations of the key terms and concepts of management in this reference work. The Encyclopedia of Sustainable Management represents all aspects of management and business conduct. It takes sustainability as a management concept that gives due credit to the complexity and diverging constraints in which businesses and corporations act today, and it emphasizes and focuses approaches that help ensure that today's management decisions and actions will be the basis for tomorrow's prosperity.

Big data are part of a paradigm shift that is significantly transforming statistical agencies, processes, and data analysis. While administrative and satellite data are already well established, the statistical community is now experimenting with structured and unstructured human-sourced, process-mediated, and machine-generated big data. The proposed SDN sets out a typology of big data for statistics and highlights that opportunities to exploit big data for official statistics will vary across countries and statistical domains. To illustrate the former, examples from a diverse set of countries are presented. To provide a balanced assessment on big data, the proposed SDN also discusses the key challenges that come with proprietary data from the private sector with regard to accessibility, representativeness, and sustainability. It concludes by discussing the implications for the statistical community going forward.

This book presents the proceedings of the 2020 2nd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2021), online conference, on 30 October 2021. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.

Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients.

The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.

Cybersecurity, or information technology security (I/T security), is the protection of computer systems and networks from information disclosure; theft of or damage to their hardware, software, or electronic data; as well as from the disruption or misdirection of the services they provide. The field is becoming increasingly critical due to the continuously expanding reliance on computer systems, the internet, wireless network standards such as Bluetooth and Wi-Fi, and the growth of "smart" devices, which constitute the internet of things (IoT). Cybersecurity is also one of the significant challenges in the contemporary world, due to its complexity, both in terms of political usage and technology. Its primary goal is to ensure the dependability, integrity, and data privacy of enterprise-wide systems in an era of increasing cyberattacks from around the world. Effective Cybersecurity Operations for Enterprise-Wide Systems examines current risks involved in the cybersecurity of various systems today from an enterprise-wide perspective. While there are multiple sources available on cybersecurity, many publications do not include an enterprise-wide perspective of the research. The book provides such a perspective from multiple sources that include investigation into critical business systems such as supply chain management, logistics, ERP, CRM, knowledge management, and others. Covering topics including cybersecurity in international business, risk management, artificial intelligence, social engineering, spyware, decision support systems, encryption, cyber-attacks and breaches, ethical hacking, transaction support systems, phishing, and data privacy, it is designed for educators, IT developers, education professionals, education administrators, researchers, security analysts, systems engineers, software security engineers, security professionals, policymakers, and students.

In the investigation Exploring the Boundaries of Big Data The Netherlands Scientific Council for Government Policy (WRR) offers building blocks for developing a regulatory approach to Big Data.

Since the first censuses were taken and crop yields recorded in ancient times, data collection and analysis have been essential to improving the functioning of society. Foundational work in calculus, probability theory, and statistics in the 17th and 18th centuries provided an array of new tools used by scientists to more precisely predict the movements of the sun and stars and determine population-wide rates of crime, marriage, and suicide. These tools often led to stunning advances. In the 1800s, Dr. John Snow used early modern data science to map cholera “clusters” in London. By tracing to a contaminated public well a disease that was widely thought to be caused by “miasmatic” air, Snow helped lay the foundation for the germ theory of disease.Gleaning insights from data to boost economic activity also took hold in American industry. Frederick Winslow Taylor's use of a stopwatch and a clipboard to analyze productivity at Midvale Steel Works in Pennsylvania increased output on the shop floor and fueled his belief that data science could revolutionize every aspect of life.2 In 1911, Taylor wrote The Principles of Scientific Management to answer President Theodore Roosevelt's call for increasing “national efficiency”: Today, data is more deeply woven into the fabric of our lives than ever before. We aspire to use data to solve problems, improve well-being, and generate economic prosperity. The collection, storage, and analysis of data is on an upward and seemingly unbounded trajectory, fueled by increases in processing power, the cratering costs of computation and storage, and the growing number of sensor technologies embedded in devices of all kinds. In 2011, some estimated the amount of information created and replicated would surpass 1.8 zettabytes. In 2013, estimates reached 4 zettabytes of data generated worldwide.