This book celebrates the 40th anniversary of the creation of the CRID and the 10th anniversary of its successor, the CRIDS. It gathers twenty-one very high quality contributions on extremely interesting and topical aspects of data protection. The authors come from Europe as well as from the United States of America and Canada. Their contributions have been grouped as follows: 1° ICT Governance; 2° Commodification & Competition; 3° Secret surveillance; 4° Whistleblowing; 5° Social Medias, Web Archiving & Journalism; 6° Automated individual decision-making; 7° Data Security; 8° Privacy by design; 9° Health, AI, Scientific Research & Post-Mortem Privacy. This book is intended for all academics, researchers, students and practitioners who have an interest in privacy and data protection.

Dix-sept contributions offrant des analyses approfondies en matière de protection des données sur des sujets qui font l'actualité en droit des technologies et de l'information.

In digital markets, data protection and competition law affect each other in diverse and intricate ways. Their entanglement has triggered a global debate on how these two areas of law should interact to effectively address new harms and ensure that the digital economy flourishes. Coherence between Data Protection and Competition Law in Digital Markets offers a blueprint for bridging the disconnect between data protection and competition law and ensuring a coherent approach towards their enforcement in digital markets. Specifically, this book focuses on the evolution of data protection and competition law, their underlying rationale, their key features and common objectives, and provides a series of examples to demonstrate how the same empirical phenomena in digital markets pose a common challenge to protecting personal data and promoting market competitiveness. A panoply of theoretical and empirical commonalities between these two fields of law, as this volume shows, are barely mirrored in the legal, enforcement, policy, and institutional approaches in the EU and beyond, where the silo approach continues to prevail. The ideas that Majcher puts forward for a more synergetic integration of data protection and competition law are anchored in the concept of 'sectional coherence'. This new coherence-centred paradigm reimagines the interpretation and enforcement of data protection and competition law as mutually cognizant and reciprocal, allowing readers to explore, in an innovative way, the interface between these legal fields and identify positive interactions, instead of merely addressing inconsistencies and tensions. This book reflects on the conceptual, practical, institutional, and constitutional implications of the transition towards coherence and the relevance of its findings for other jurisdictions.

The right to privacy is a fundamental right. Along with the related right to personal data protection, it has come to take a central place in contemporary employment relations and shows significant relevance for the future of work. This thoroughly researched volume, which offers insightful essays by leading European academics and policymakers in labour and employment law, is the first to present a thoroughly up-to-date Europe-wide survey and analysis of the intensive and growing interaction of workplace relations systems with developments in privacy law. With abundant reference to the EU’s General Data Protection Regulation, the case law of the European Court of Human Rights, and the work of the International Labour Organisation, the book proceeds as a series of country chapters, each by a recognised expert in a specific jurisdiction. Legal comparison is based on a questionnaire circulated to the contributors in advance. Each country chapter addresses the national legal weight of such issues and topics as the following: interaction of privacy and data protection law; legitimacy, purpose limitation, and data minimisation; transparency; role of consent; artificial intelligence and automated decision-making; health-related data, including biometrics and psychological testing; monitoring and surveillance; and use of social media. A detailed introductory overview begins the volume. The research for this book is based on a dynamic methodology, founded in scientific desk research and expert networking. Recognising that the need for further guidance for privacy at work has been demonstrated by various European and international bodies, this book delivers a signal contribution to the field for social partners, practitioners, policymakers, scholars, and all other stakeholders working at the crossroads of privacy, data protection, and labour law.

Derived from the renowned multi-volume International Encyclopaedia of Laws, this convenient volume provides comprehensive analysis of the law at EU level affecting the physician-patient relationship and the interaction of physicians with other healthcare providers and the healthcare system. Although the legal aspects of healthcare in Europe most often fall under national law, the past two decades have witnessed the emergence of a distinctive field of EU health law with its own underlying principles and structural coherence, founded in a series of directives and CJEU decisions. This book examines the areas in which EU law now must be taken into account in healthcare, including aspects of patients’ rights, recognition of professional qualifications and minimum training conditions, professional rules of conduct, clinical trials and investigations of medicinal products and medical devices, health and genetic data, and beginning and end of life issues. Succinct and practical, this book will prove to be of great value to professional organizations of physicians, nurses, hospitals, and relevant government agencies. Lawyers representing parties with interests in the European Union will welcome this very useful guide, and academics and researchers will appreciate its comparative value as a contribution to the study of health law and medical law in the international context.

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 is the first book to span the depth between traditional sport diving editions and the complex medical/commercial texts. It provides a balanced view of the fascinations and hazards of deep diving through extensive factual development of its technical chapters.