Data Protection [electronic resource] : The Wake of AI and Machine Learning / edited by Chaminda Hewage, Lasith Yasakethu, Dushantha Nalin K. Jayakody.

Chapter 1 The data protection challenges and opportunities due to the emerging AI and ML technologies -- Chapter 2: Death becomes data -- Chapter 3 Redefining Reality in Political Propaganda: Exploring the Impact of Superimposed Deepfakes in Misinformation Campaigns -- Chapters 4 Profiling and Privacy: The Responsibility for Data Privacy in the Wake of Advancing Technologies -- Chapter 5 Assessing the Application of Artificial Intelligence and Machine Learning in Detecting Misinformation and Disinformation -- Chapter 6 Trust and trustworthiness: Privacy Protection in the ChatGPT Era -- Chapter 7 Beyond the Black Box: XAI Strategies for Safeguarding Critical Infrastructure -- Chapter 8 Data Protection Challenges in the Processing of Sensitive Data -- Chapter 9 Metaverse Meets Robotics: Addressing Data Protection and Privacy in Robotic Environment -- Chapter 10 Detecting Deepfakes Through the Classification of Facial Active and Passive Features Using Machine Learning -- Chapter 11 Smart Cities, Secure Data: Navigating the Evolving Landscape of Data Protection Challenges -- Chapter 12 Disinformation and the Impact on Democracy.

This book provides a thorough and unique overview of the challenges, opportunities and solutions related with data protection in the age of AI and ML technologies. It investigates the interface of data protection and new technologies, emphasizing the growing need to safeguard personal and confidential data from unauthorised access and change. The authors emphasize the crucial need of strong data protection regulations, focusing on the consequences of AI and ML breakthroughs for privacy and individual rights. This book emphasizes the multifarious aspect of data protection, which goes beyond technological solutions to include ethical, legislative and societal factors. This book explores into the complexity of data protection in the age of AI and ML. It investigates how massive volumes of personal and sensitive data are utilized to train and develop AI models, demanding novel privacy-preserving strategies such as anonymization, differential privacy and federated learning. The duties and responsibilities of engineers, policy makers and ethicists in minimizing algorithmic bias and ensuring ethical AI use are carefully defined. Key developments, such as the influence of the European Union's General Data Protection Regulation (GDPR) and the EU AI Act on data protection procedures, are reviewed critically. This investigation focusses not only on the tactics used, but also on the problems and successes in creating a secure and ethical AI ecosystem. This book provides a comprehensive overview of the efforts to integrate data protection into AI innovation, including valuable perspectives on the effectiveness of these measures and the ongoing adjustments required to address the fluid nature of privacy concerns. This book is a helpful resource for upper-undergraduate and graduate computer science students, as well as others interested in cybersecurity and data protection. Researchers in AI, ML, and data privacy as well as data protection officers, politicians, lawmakers and decision-makers will find this book useful as a reference.

UABC ; Perpetuidad