Data protection in a post-pandemic society : law regulations, best practices and recent solutions / Chaminda Hewage, Yogachandran Rahulamathavan, Deepthi Ratnayake, editors.
Hewage, Chaminda.; Rahulamathavan, Yogachandran.; Ratnayake, Deepthi.
2023
K3264.C65 D38 2023
Available Online

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Title Data protection in a post-pandemic society : law regulations, best practices and recent solutions / Chaminda Hewage, Yogachandran Rahulamathavan, Deepthi Ratnayake, editors.
ISBN 9783031340062 electronic book
303134006X electronic book
3031340051
9783031340055
DOI https://doi.org/10.1007/978-3-031-34006-2
Published Cham, Switzerland : Springer, [2023]
Language English
Description 1 online resource
Other Standard Identifiers 10.1007/978-3-031-34006-2 doi
Call Number K3264.C65 D38 2023
Dewey Decimal Classification 342.0858
Summary 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. .
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Description based on online resource; title from digital title page (viewed on August 18, 2023).
Added Author Hewage, Chaminda.
Rahulamathavan, Yogachandran.
Ratnayake, Deepthi.
Available in Other Form Print version: 9783031340055
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Intro
Preface
Contents
About the Editors
Post-Covid-19 Metaverse Cybersecurity and Data Privacy: Present and Future Challenges
1 Introduction
2 Previous Work
3 Research Challenges
Cybersecurity
Avatar Integrity
Device Security
Data Privacy
Data Collection
User Consent
Direct Marketing
Data Intermediaries
Health
Content Moderation
Children
User Education
Economy
Ownership
Advertising
Portability and Interoperability
Transparency and Accountability: AI
Laws and Regulation Landscape

EU/UK General Data Protection Regulations
Confidentiality
Responsibility and Liability
EU Digital Services Act
Consistency
Systematisation
Consumers Vs Traders
Transparency
UK Online Safety Bill
Big Tech Companies
UK Two-Tier System: Liability
Publishers
Online Intermediaries
Legal Clarity
Discussion and Summary
4 Future Research Directions and Potential Solutions for Cybersecurity
Avatar Integrity
Security Protocols
Cyber-Resilience
Data Privacy
Data Collection
Metaverse: Open and Decentralised
Data Protection Framework
Health

Content Moderation
Children
User Education
Economy
Ownership
Advertising
Data Portability and Interoperability
Transparency and Accountability: Blockchain
Laws and Regulation Landscape
EU/UK General Data Protection Regulations
Confidentiality
Responsibility and Liability
EU Digital Services Act
Consistency
Systematisation
Consumer Vs Trader
Transparency
UK Online Safety Bill
Big Tech Companies
UK Two-Tier System: Liability
Publishers
Online Intermediaries
Legal Clarity
5 Discussion
6 Conclusion
Recommendations

Policy and Notification
VDaaS: Notification, Consent, Policies, and Records System Update
Data Provenance and Integrity
Data Veracity and User Safety
Cybersecurity
AI Training Data
Data Privacy
Due Diligence and Best Practices
Avatars
User Consent and Age Verification
Tokenization-Validation
Limitations
Conflict of Interest
References
Keeping it Low-Key: Modern-Day Approaches to Privacy-Preserving Machine Learning
1 Introduction
2 The Great Privacy Awakening
3 Attacks on ML Systems
Data Access Attack
Membership Inference Attack

Input Inference Attack
Parameter Inference Attack
Property Inference Attack
4 Quantifying Privacy Risks in ML Systems
Membership Inference
Input Inference
Parameter Inference
Property Inference
5 Privacy-Preserving Machine Learning
6 Privacy-Preserving Techniques
Differential Privacy
Federated Learning
Synthetic Data
Data Condensation
Auxiliary Techniques
7 Privacy and Responsible ML
8 Conclusion
References
Security Analysis of Android Hot Cryptocurrency Wallet Applications
1 Introduction
Background/Context
Research Focus and Purpose

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