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Data protection in a post-pandemic s...

Hewage, Chaminda.

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Data protection in a post-pandemic society = laws, regulations, best practices and recent solutions /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data protection in a post-pandemic society/ edited by Chaminda Hewage, Yogachandran Rahulamathavan, Deepthi Ratnayake.
其他題名:	laws, regulations, best practices and recent solutions /
其他作者:	Hewage, Chaminda.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xiv, 239 p. :ill., digital ;24 cm.
內容註:	Chapter. 1. Post-Covid-19 Metaverse Cybersecurity and Data Privacy-Present and Future Challenges -- Chapter. 2. Keeping it Low-Key: Modern-day approaches to Privacy-Preserving Machine Learning -- Chapter. 3. Security Analysis of Android Hot Cryptocurrency Wallet Applications -- Chapter. 4. Exploring the Opportunities of Applying Digital Twins for Intrusion Detection in Industrial Control Systems of Production and Manufacturing - A Systematic Review -- Chapter. 5. Securing Privacy During a World Health Emergency: Exploring How to Create a Balance Between the Need to Save the World and People's Right to Privacy -- Chapter. 6. Federated Learning: Data privacy and cyber security in edge-based machine learning Mobile Malware Detection Using Consortium -- Chapter. 7. Emerging Computer Security Laws and Regulations across the Globe: A Comparison between Sri Lankan and Contemporary International Computer Acts -- Chapter. 8. Legal Considerations and Ethical Challenges of Artificial Intelligence on Internet of Things and Smart Cities.
Contained By:	Springer Nature eBook
標題:	Data protection - Law and legislation. -
電子資源:	https://doi.org/10.1007/978-3-031-34006-2
ISBN:	9783031340062

Data protection in a post-pandemic society = laws, regulations, best practices and recent solutions /
Data protection in a post-pandemic societylaws, regulations, best practices and recent solutions /[electronic resource] :edited by Chaminda Hewage, Yogachandran Rahulamathavan, Deepthi Ratnayake. - Cham :Springer International Publishing :2023. - xiv, 239 p. :ill., digital ;24 cm.

Chapter. 1. Post-Covid-19 Metaverse Cybersecurity and Data Privacy-Present and Future Challenges -- Chapter. 2. Keeping it Low-Key: Modern-day approaches to Privacy-Preserving Machine Learning -- Chapter. 3. Security Analysis of Android Hot Cryptocurrency Wallet Applications -- Chapter. 4. Exploring the Opportunities of Applying Digital Twins for Intrusion Detection in Industrial Control Systems of Production and Manufacturing - A Systematic Review -- Chapter. 5. Securing Privacy During a World Health Emergency: Exploring How to Create a Balance Between the Need to Save the World and People's Right to Privacy -- Chapter. 6. Federated Learning: Data privacy and cyber security in edge-based machine learning Mobile Malware Detection Using Consortium -- Chapter. 7. Emerging Computer Security Laws and Regulations across the Globe: A Comparison between Sri Lankan and Contemporary International Computer Acts -- Chapter. 8. Legal Considerations and Ethical Challenges of Artificial Intelligence on Internet of Things and Smart Cities.

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.

ISBN: 9783031340062

Standard No.: 10.1007/978-3-031-34006-2doiSubjects--Topical Terms:

661635
Data protection
--Law and legislation.

LC Class. No.: K3264.C65 / D38 2023

Dewey Class. No.: 342.0858

Data protection in a post-pandemic society = laws, regulations, best practices and recent solutions /
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520 $a 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.
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