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Types of Privacy Expectations
Ashwini Rao1, Juergen Pfeffer1
1Technical University of Munich, Munich, Germany.
Frontiers in Big Data
|March 11, 2021
Summary
User privacy expectations are multifaceted, not a single concept. This study identifies four distinct types of privacy expectations, crucial for accurate measurement and compliance with regulations like the General Data Protection Regulation (GDPR).
Area of Science:
- Information Science
- Human-Computer Interaction
- Psychology
Background:
- Assessing user privacy expectations is critical for regulatory compliance, such as under the General Data Protection Regulation (GDPR).
- Previous research has predominantly treated privacy expectations as a singular, undifferentiated construct.
- This approach overlooks the nuanced nature of how individuals perceive and value their privacy.
Purpose of the Study:
- To propose and validate a multi-level conceptual model of user privacy expectations.
- To differentiate between four distinct types of privacy expectations: Desired, Predicted, Deserved, and Minimum.
- To establish a hierarchy among these distinct privacy expectation types.
Main Methods:
- Developed a conceptual model of privacy expectation based on existing theories.
- Conducted a within-subjects empirical study involving 1,249 participants representative of the US online population.
- Analyzed participant ratings of privacy expectations in a scenario concerning health-related browsing data collection by a bank.
Main Results:
- Empirical validation confirmed that individuals possess distinct types of privacy expectations.
- Approximately one-third of the population differentiated between Predicted and Minimum privacy expectation types.
- Significant demographic variations, particularly between younger (18-29) and older (60+) adults, were observed in these distinctions.
Conclusions:
- User privacy expectations are demonstrably multi-level, comprising distinct types.
- Future research and practical applications must explicitly account for these different types of privacy expectations for accurate assessment.
- Understanding these nuances is vital for effective privacy policy development and user trust.
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