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Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data

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Cover of 'Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data'

Table of Contents

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    Book Overview
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    Chapter 1 A Causal Bayesian Networks Viewpoint on Fairness
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    Chapter 2 Sharing Is Caring, a Boundary Object Approach to Mapping and Discussing Personal Data Processing
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    Chapter 3 Who You Gonna Call When There’s Something Wrong in Your Processing? Risk Assessment and Data Breach Notifications in Practice
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    Chapter 4 Design and Security Assessment of Usable Multi-factor Authentication and Single Sign-On Solutions for Mobile Applications
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    Chapter 5 Towards Empowering the Human for Privacy Online
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    Chapter 6 Trust and Distrust: On Sense and Nonsense in Big Data
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    Chapter 7 GDPR Transparency Requirements and Data Privacy Vocabularies
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    Chapter 8 Wider Research Applications of Dynamic Consent
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    Chapter 9 Glycos: The Basis for a Peer-to-Peer, Private Online Social Network
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    Chapter 10 GDPR and the Concept of Risk :
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    Chapter 11 Privacy Patterns for Pseudonymity
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    Chapter 12 Implementing GDPR in the Charity Sector: A Case Study
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    Chapter 13 Me and My Robot - Sharing Information with a New Friend
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    Chapter 14 chownIoT: Enhancing IoT Privacy by Automated Handling of Ownership Change
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    Chapter 15 Is Privacy Controllable?
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    Chapter 16 Assessing Theories for Research on Personal Data Transparency
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    Chapter 17 Data Protection by Design for Cross-Border Electronic Identification: Does the eIDAS Interoperability Framework Need to Be Modernised?
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    Chapter 18 Risk Profiling by Law Enforcement Agencies in the Big Data Era: Is There a Need for Transparency?
Overall attention for this book and its chapters
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Mentioned by

2 news outlets
39 tweeters

Readers on

25 Mendeley
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Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data
Published by
Springer International Publishing, August 2019
DOI 10.1007/978-3-030-16744-8
978-3-03-016743-1, 978-3-03-016744-8

Kosta, Eleni, Pierson, Jo, Slamanig, Daniel, Fischer-Hübner, Simone, Krenn, Stephan

Twitter Demographics

The data shown below were collected from the profiles of 39 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 20%
Student > Bachelor 4 16%
Researcher 3 12%
Student > Doctoral Student 3 12%
Student > Ph. D. Student 2 8%
Other 1 4%
Unknown 7 28%
Readers by discipline Count As %
Computer Science 10 40%
Engineering 3 12%
Business, Management and Accounting 2 8%
Social Sciences 2 8%
Economics, Econometrics and Finance 1 4%
Other 0 0%
Unknown 7 28%