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[null,"1.","Record Nr.",null,"UNISA996601561303316"]
[null,null,"Autore",null,"Rannenberg Kai"]
[null,null,"Titolo",null,"Privacy Technologies and Policy : 11th Annual Privacy Forum, APF 2023, Lyon, France, June 1-2, 2023, Proceedings"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing AG, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"3-031-61089-X"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (187 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Series ; ; v.13888"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"Requests -- 4.1 Past: First Request in 2018 -- 4.2 Present: Second Request in 2023 -- 4.3 From Past to Present -- 5 Discussion."]
[{"$nulls":4},"5.1 Completeness, Accuracy, and Transparency -- 5.2 Data Minimization -- 5.3 Understandable vs. Machine-Readable -- 5.4 Advertisement and Tracking -- 5.5 Comparison with Twitter's Privacy Statement -- 6 Conclusion and Outlook -- A  Definitions -- B  Comparison of Past and Current Files -- References -- Modelling Data Protection and Privacy -- No Children in the Metaverse? The Privacy and Safety Risks of Virtual Worlds (and How to Deal with Them) -- 1 Introduction -- 2 What is the \"Metaverse\"? -- 3 Relevance and Risks to Children -- 4 Overarching Child Privacy and Safeguarding Legal Framework -- 5 Design of Virtual Reality Devices -- 6 Design of the Virtual Spaces in the Metaverse -- 6.1 PETs (and Other 'Privacy by Law' Initiatives) -- 6.2 DPIA -- 7 Could Consumer Laws Be the/an Answer? -- 8 Data Protection Challenges in the Metaverse -- 8.1 Territorial Scope -- 8.2 Consent -- 8.3 Necessity for the Performance of a Contract and Legitimate Interest -- 8.4  Augmented Privacy Notice -- 9 Safety of Children in the Metaverse -- 9.1 Child Exploitation -- 9.2 Gambling -- 10 Broader Policy Options -- 11 Conclusion -- References -- Home Alone? Exploring the Geographies of Digitally-Mediated Privacy Practices at Home During the COVID-19 Pandemic -- 1 Introduction -- 2 Conceptual Discussions -- 2.1 Privacy is Collective -- 2.2 Privacy is Contextual -- 2.3 Privacy is Spatial -- 2.4 Privacy in the Pandemic -- 3 Conceptual Framework -- 4 Methods -- 4.1 Research Design: Semi-structured Interviews -- 4.2 Data Analysis: Thematic Analysis -- 5 Results and Discussion -- 5.1 The Blurring of the Work-Life Divide -- 5.2 Collective Dimensions of Privacy in Times of Pandemic -- 6 Future Research and Conclusions -- 6.1 Further Research and Practice Recommendations -- 6.2 Conclusions -- A Interview Questions -- A.1 Introduction -- A.2 Information Disclosure."]
[{"$nulls":4},"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."]