Human Factors in Privacy Research.

• Author: Gerber, Nina.
• TeilnehmendeR: Stöver, Alina.; Marky, Karola.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2023.; ©2023.
• Year of Publication: 2023
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (380 pages)

Table of Contents

• Intro
• Foreword
• Acknowledgements
• About This Book
• Contents
• Part I Theory
• Data Collection Is Not Mostly Harmless: An Introduction to Privacy Theories and Basics
• 1 Introduction
• 2 Privacy Theories
• 2.1 How (Not) to Define Privacy
• 3 Why Do We Need Privacy?
• References
• From the Privacy Calculus to Crossing the Rubicon: An Introduction to Theoretical Models of User Privacy Behavior
• 1 Introduction
• 2 Homo Economicus
• 3 Antecedents → Privacy Concerns → Outcomes (APCO) Model
• 4 Theory of Planned Behavior
• 5 Cognitive Consistency Theories
• 6 Transactional Model of Stress and Coping
• 7 Rubicon Model
• 8 Capability, Opportunity, Motivation → Behavior (COM-B) System
• 9 Health Action Process Approach
• 10 Conclusion
• References
• Part II Methodology
• Empirical Research Methods in Usable Privacy and Security
• 1 Introduction
• 2 Research Methods in UPS Studies
• 2.1 Systematic Literature Reviews
• 2.2 Interviews
• 2.3 Focus Groups
• 2.4 Co-Creation Methods
• 2.5 Surveys
• 2.6 Analyzing Measurement Data (Data Logs)
• 2.7 Extracting Online Datasets
• 2.8 Experience Sampling Method
• 2.9 Experiments
• 3 Techniques that Can Be Used in Combination with Methods
• 4 Participant Recruitment
• 5 Basics of Ethical Research Design with Human Participants
• 5.1 Ethical Core Principles
• 5.2 Ethical Considerations for Deceptive Research
• 5.3 Ethical Review Boards
• 6 Biases in Research with Human Participants
• 7 Conclusion
• References
• Toward Valid and Reliable Privacy Concern Scales: The Example of IUIPC-8
• 1 Introduction
• 2 Information Privacy Concern
• 2.1 What Is Information Privacy Concern?
• 2.2 Information Privacy Concern Instruments
• 3 Validity and Reliability
• 3.1 Construct Validity
• 3.2 Reliability
• 4 Factor Analysis as Tool to Establish Measurement Instruments.
• 4.1 Estimation Methods for Ordinal Non-normal Data
• 4.2 Comparing Nested Models
• 4.3 Global and Local Fit
• 5 Approach
• 5.1 Analysis Methodology
• 5.2 Sample
• 5.3 Validity and Reliability Criteria
• 6 The Validation of IUIPC-8
• 6.1 Sample
• 6.2 Descriptives
• 6.3 Construct Validity
• Factorial Validity
• Model Fit
• CFA Model, Convergent, and Discriminant Validity
• 6.4 Reliability: Internal Consistency
• 7 Discussion
• 8 Summary
• Appendix
• Materials and Sample
• Thresholds
• References
• Achieving Usable Security and Privacy Through Human-Centered Design
• 1 Introduction
• 2 Background
• 2.1 Human-Centered Design
• 2.2 Usable Security and Privacy
• 3 Mental Models in Security and Privacy
• 3.1 Mental Models in Human-Computer Interaction
• 3.2 Mental Models in Usable Security and Privacy
• 3.3 Mental Model Elicitation
• 4 Usable Security and Privacy Needs
• 4.1 USP Needs as a Requirements Type
• 4.2 USP Needs Elicitation and Analysis
• 4.3 USP Needs Documentation and Validation
• 4.4 Example Case Study
• 5 User Group Profiles and Privacy Personas
• 5.1 User Group Profiles
• 5.2 Privacy Personas
• 6 Summary and Conclusion
• References
• What HCI Can Do for (Data Protection) Law-Beyond Design
• 1 Introduction
• 2 The Call for Effective Measures: A Door Opener for Empirical Sciences
• 3 Going Beyond Designing Law: The Case for the Full Toolbox of HCI Research
• 4 Levels of Engagement: How HCI and Law Can Make Data Protection More Effective
• 4.1 Case 1: Cookie Banners
• 4.2 Case 2: Data Subject Rights
• 4.3 Implementation: What Can Design Do for Law?
• 4.4 Evaluation: How Well Is Law Currently Working?
• 4.5 Identification: Challenging Existing Legal Interpretations and Concepts
• 5 The Road Ahead
• References
• Expert Opinions as a Method of Validating Ideas: Applied to Making GDPR Usable.
• 1 Introduction
• 2 Method
• 2.1 Collecting Interview Data
• 2.2 Participants
• 2.3 Thematic Analysis
• 3 The Need to Evaluate and Measure Usability of Privacy
• 3.1 Evaluating Usability of Privacy
• 3.2 Measuring Usability of Privacy
• 4 Usable Privacy Definition Adapts Well ISO 9241-11:2018
• 5 A Comprehensive List of Usable Privacy Goals
• 6 Ways to Meet the Usable Privacy Criteria
• 7 Usable Privacy Cube Model as an Abstraction of Known and Implied Principles of Privacy Evaluations
• 8 Summarizing the Results of the Validation Study
• 9 Conclusion
• References
• Part III Application Areas
• Privacy Nudges and Informed Consent? Challenges for Privacy Nudge Design
• 1 Introduction to Nudging
• 2 An Overview on Privacy Nudges
• 3 Ethical Considerations
• 4 Challenges of Designing Privacy Nudges
• 5 Discussion of Approaches
• 5.1 Design of Privacy-Preserving Nudges
• 5.2 Design of Nudges that Target Reflective Thinking
• 5.3 Ask the Users
• 5.4 Choose a Combination of Approaches
• 6 Summary
• References
• The Hows and Whys of Dark Patterns: Categorizations and Privacy
• 1 Introduction
• 2 Dark Patterns
• 2.1 Why Do Dark Patterns Work?
• Heuristics and Biases
• 2.2 Privacy Decision-Making
• 2.3 Categorization of Dark Patterns
• 3 Privacy Dark Patterns
• 3.1 Examples of Privacy Dark Patterns
• Invisible to the Human Eye
• UI Design Tricks
• Constrained Actionability
• Emotion-Related
• Affecting Comprehension
• Time-Related
• Affecting Privacy Options
• 3.2 Tackling (Privacy) Dark Patterns
• 3.3 Dark Patterns and Implications on Businesses
• 4 Concluding Remarks
• References
• ``They see me scrollin''-Lessons Learned from Investigating Shoulder Surfing Behavior and Attack Mitigation Strategies
• 1 Introduction
• 2 Investigating the Phenomenon
• 2.1 Defining Shoulder Surfing (Attacks).
• 2.2 Research Methods
• 2.3 Key Findings on Shoulder Surfing Behavior
• 3 Mitigating Shoulder Surfing Attacks
• 3.1 Threat Models
• 3.2 Algorithmic Detection of Attacks
• 3.3 Prevention Strategies
• 4 Challenges and Future Research Directions
• 5 Conclusion
• References
• Privacy Research on the Pulse of Time: COVID-19 Contact-Tracing Apps
• 1 Introduction
• 2 Tracing Technologies
• 2.1 Proximity Tracing
• 2.2 Risk Calculation and Informing Those at Risk
• 3 Privacy and Contact Tracing Apps-User Studies
• 3.1 Results from User Studies-Privacy Concerns
• 3.2 Influence of Privacy on Using a CTA
• 4 Privacy: A Matter of Asking? Looking at Different Methods
• 4.1 Timing and Context
• 4.2 Who Is Asked?
• 4.3 Privacy Concerns != Privacy Concerns
• 5 Conclusion
• References
• Privacy Perception and Behavior in Safety-Critical Environments
• 1 Introduction
• 2 On the Relationship Between Cyber Privacy and Security Behavior
• 3 Awareness on Data Sharing Functionalities and Acceptance of Private Data Sharing
• 4 Critical Environment I: Digital Privacy Perceptions of Asylum Seekers in Germany
• 5 Critical Environment II: The Role of Privacy in Digitalization-Analyzing Perspectives of German Farmers
• 6 Conclusion
• References
• Part IV Solutions
• Generic Consents in Digital Ecosystems: Legal, Psychological, and Technical Perspectives
• 1 Challenge and Vision
• 2 Generic Consents
• 3 Legal Assessment
• 3.1 Personal Information Management Systems in Digital Ecosystems
• 3.2 Obtaining Consent via a PIMS
• 3.3 Using Allowlists in Digital Ecosystems
• Solution 1: Organizational Allowlists
• Solution 2: User-Defined Allowlists
• 3.4 Legal Conclusion
• 4 User-Oriented Redesign of Consent Handling
• 4.1 Psychological Effects of Cookie Banners
• Problem 1: Upfront Consents
• Problem 2: Coerced Consents.
• Problem 3: Poor User Experience
• Problem 4: Unclear Utility
• Problem 5: Dark Patterns
• Problem 6: Repeated Consents
• 4.2 Solutions for Improved User Experience
• Solution 1: Make Cookies Something of Later Concern
• Solution 2: Reject Until Further Notice
• Solution 3: Provide Differentiated Decision Support
• Solution 4: Encourage Decision Review
• 5 Feasibility of Technical Implementation
• 5.1 Consent Representation Formats
• 5.2 Consent Forwarding
• 5.3 Data Forwarding
• 6 Discussion
• 6.1 Allowlists Created by NGOs (Solution 1)
• 6.2 Allowlists Created by the User (Solution 2)
• 6.3 Blocklists
• 6.4 Usability
• 7 Conclusion
• References
• Human-Centered Design for Data-Sparse Tailored Privacy Information Provision
• 1 Motivation
• 2 Overview of Extant Transparency-Enhancing Technologies
• 2.1 Tailoring Potential of Transparency-Enhancing Technologies
• 3 Solution Space for Tailoring Challenges
• 3.1 Privacy Preferences
• 3.2 Technical Privacy-Preserving Mechanisms
• 4 Solution Archetypes for Tailored Privacy Information Provision
• 4.1 Suitability of Tailoring Approaches
• 4.2 Feasibility of Local and Remote Processing
• 5 Conclusions
• References
• Acceptance Factors of Privacy-Enhancing Technologies on the Basis of Tor and JonDonym
• 1 Introduction and Background
• 2 Methodology
• 2.1 Questionnaire Composition
• 2.2 Questionnaire Data Collection
• 2.3 Questionnaire Evaluation
• Quantitative Methods
• Qualitative Methods
• 2.4 Interview Data Collection
• 2.5 Interview Evaluation
• 3 Results
• 3.1 Internet Users Information Privacy Concerns
• 3.2 Technology Acceptance Model
• 3.3 Evaluation of Open Questions
• 3.4 Customers' Willingness to Pay or Donate
• 3.5 Companies' Incentives and Hindrances to Implement PETs
• 4 Discussion and Conclusion
• References.
• Increasing Users' Privacy Awareness in the Internet of Things: Design Space and Sample Scenarios.