Privacy Technologies and Policy
11th Annual Privacy Forum, APF 2023, Lyon, France, June 1-2, 2023, Proceedings
Published on 29. May 2024
X, 183 pages
978-3-031-61089-9 (ISBN)
Description
This book constitutes the refereed conference proceedings of the 11th Annual Privacy Forum, APF 2023 in Lyon, France in June 2023.
The 8 full papers were carefully reviewed and selected from 37 submissions. The papers are organized in the following topical sections: Emerging Technologies and Protection of Personal Data, Data Protection Principles and Data Subject Rights, Modelling Data Protection and Privacy, and Modelling Perceptions of Privacy.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Product notice
Reflowable
Illustrations
X, 183 p. 27 illus., 17 illus. in color.
File size
7,59 MB
ISBN-13
978-3-031-61089-9 (9783031610899)
DOI
10.1007/978-3-031-61089-9
Schweitzer Classification
Other editions
Additional editions
Kai Rannenberg | Prokopios Drogkaris | Cédric Lauradoux
Privacy Technologies and Policy
11th Annual Privacy Forum, APF 2023, Lyon, France, June 1-2, 2023, Proceedings
Book
05/2024
Springer
€128.39
Shipment within 15-20 days
Content
- Intro
- Preface
- Organization
- Contents
- Emerging Technologies and Protection of Personal Data
- A Universal Data Model for Data Sharing Under the European Data Strategy
- 1 Introduction
- 2 Background
- 2.1 The European Data Strategy
- 2.2 The Data Governance Act and Data Intermediaries
- 2.3 Digital Markets Act and Market Fairness
- 3 Related Work
- 4 Data Model
- 4.1 Requirements
- 4.2 Classes and Attributes
- 5 Illustrating Example
- 6 Application Areas
- 7 Discussion and Open Issues
- 8 Conclusion
- References
- A Decision-Making Process to Implement the 'Right to Be Forgotten' in Machine Learning
- 1 Introduction
- 1.1 Paper Contributions
- 1.2 Paper Structure
- 2 Background
- 2.1 Machine Learning
- 2.2 Literature Review
- 3 Decision-Making Flow
- 3.1 Legal Requirements for Implementing the RTBF
- 3.2 Machine Learning Techniques for Erasure
- 4 Conclusion
- References
- Data Protection Principles and Data Subject Rights
- A Data Protection-Compliant Framework for Wi-Fi-Based Location Tracking (in Law Enforcement)
- 1 Introduction
- 2 Related Work
- 3 System and Scenario for a Domestic Wi-Fi Detector
- 3.1 Limitation of the Perimeter
- 3.2 Limitation of the Duration of Data Collection and Storage
- 3.3 Detection of Authorised and Unauthorised Devices
- 3.4 Lawfulness of Processing
- 3.5 Information Obligations - A Legal Minefield
- 3.6 Opt-Out Possibilities
- 4 Conclusion
- References
- Past and Present: A Case Study of Twitter's Responses to GDPR Data Requests
- 1 Introduction
- 2 Related Work
- 2.1 General Data Protection Regulation
- 2.2 Research Approaches Related to GDPR
- 2.3 Summary
- 3 Methodology
- 4 Evaluation of Data Requests
- 4.1 Past: First Request in 2018
- 4.2 Present: Second Request in 2023
- 4.3 From Past to Present
- 5 Discussion
- 5.1 Completeness, Accuracy, and Transparency
- 5.2 Data Minimization
- 5.3 Understandable vs. Machine-Readable
- 5.4 Advertisement and Tracking
- 5.5 Comparison with Twitter's Privacy Statement
- 6 Conclusion and Outlook
- A Definitions
- B Comparison of Past and Current Files
- References
- Modelling Data Protection and Privacy
- No Children in the Metaverse? The Privacy and Safety Risks of Virtual Worlds (and How to Deal with Them)
- 1 Introduction
- 2 What is the "Metaverse"?
- 3 Relevance and Risks to Children
- 4 Overarching Child Privacy and Safeguarding Legal Framework
- 5 Design of Virtual Reality Devices
- 6 Design of the Virtual Spaces in the Metaverse
- 6.1 PETs (and Other 'Privacy by Law' Initiatives)
- 6.2 DPIA
- 7 Could Consumer Laws Be the/an Answer?
- 8 Data Protection Challenges in the Metaverse
- 8.1 Territorial Scope
- 8.2 Consent
- 8.3 Necessity for the Performance of a Contract and Legitimate Interest
- 8.4 Augmented Privacy Notice
- 9 Safety of Children in the Metaverse
- 9.1 Child Exploitation
- 9.2 Gambling
- 10 Broader Policy Options
- 11 Conclusion
- References
- Home Alone? Exploring the Geographies of Digitally-Mediated Privacy Practices at Home During the COVID-19 Pandemic
- 1 Introduction
- 2 Conceptual Discussions
- 2.1 Privacy is Collective
- 2.2 Privacy is Contextual
- 2.3 Privacy is Spatial
- 2.4 Privacy in the Pandemic
- 3 Conceptual Framework
- 4 Methods
- 4.1 Research Design: Semi-structured Interviews
- 4.2 Data Analysis: Thematic Analysis
- 5 Results and Discussion
- 5.1 The Blurring of the Work-Life Divide
- 5.2 Collective Dimensions of Privacy in Times of Pandemic
- 6 Future Research and Conclusions
- 6.1 Further Research and Practice Recommendations
- 6.2 Conclusions
- A Interview Questions
- A.1 Introduction
- A.2 Information Disclosure
- A.3 Changes in Information Disclosure
- A.4 General Privacy Practices
- A.5 The Importance of Privacy
- A.6 Internet Tools for Fighting COVID-19
- A.7 Wrap-Up
- B Code Book
- C Interview Question Formulation
- References
- Modelling Perceptions of Privacy
- From Dark Patterns to Fair Patterns? Usable Taxonomy to Contribute Solving the Issue with Countermeasures
- 1 Introduction
- 2 Current State of Play on Dark Patterns: Prevalence, Evidence of Harms and Legal Framework
- 2.1 Definitions and Prevalence
- 2.2 Evidence of Serious Individual and Structural Harms
- 2.3 Legal Framework
- 3 Usable Taxonomy to Empower All Stakeholders to Take Action and Fight Against Dark Patterns
- 3.1 Related Work
- 3.2 Proposal of a Usable Taxonomy
- 4 Discussion
- 5 Conclusion
- References
- A Singular Approach to Address Privacy Issues by the Data Protection and Privacy Relationships Model (DAPPREMO)
- 1 Introduction
- 2 Application of the Set Theory
- 3 The Relationships Between Objects and Those Between Subassemblies
- 4 Description of a Complex Multidimensional Model
- 5 The Model and the Role of the Subjects
- 6 DAPPREMO's Concrete Applications for Individuals and Institutions, and Future Developments: The Use of Artificial Intelligence
- 7 Conclusions
- References
- Author Index
