Chaminda Hewage – författare

This book offers the latest research results and predictions in data protection with a special focus on post-pandemic society. This book also includes various case studies and applications on data protection. It includes the Internet of Things (IoT), smart cities, federated learning, Metaverse, cryptography and cybersecurity. Data protection has burst onto the computer security scene due to the increased interest in securing personal data. 

Data protection is a key aspect of information security where personal and business data need to be protected from unauthorized access and modification. The stolen personal information has been used for many purposes such as ransom, bullying and identity theft. Due to the wider usage of the Internet and social media applications, people make themselves vulnerable by sharing personal data. This book discusses the challenges associated with personal data protection prior, during and post COVID-19 pandemic. Some of these challenges are caused by the technological advancements (e.g. Artificial Intelligence (AI)/Machine Learning (ML) and ChatGPT). 

In order to preserve the privacy of the data involved, there are novel techniques such as zero knowledge proof, fully homomorphic encryption, multi-party computations are being deployed. The tension between data privacy and data utility drive innovation in this area where numerous start-ups around the world have started receiving funding from government agencies and venture capitalists. This fuels the adoption of privacy-preserving data computation techniques in real application and the field is rapidly evolving. Researchers and students studying/working in data protection and related security fields will find this book useful as a reference.

 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, emphasising 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.