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Title: A Design Space for Privacy Choices: Towards Meaningful Privacy Control in the Internet of Things
“Notice and choice” is the predominant approach for data privacy protection today. There is considerable user-centered research on providing effective privacy notices but not enough guidance on designing privacy choices. Recent data privacy regulations worldwide established new requirements for privacy choices, but system practitioners struggle to implement legally compliant privacy choices that also provide users meaningful privacy control. We construct a design space for privacy choices based on a user-centered analysis of how people exercise privacy choices in real-world systems. This work contributes a conceptual framework that considers privacy choice as a user-centered process as well as a taxonomy for practitioners to design meaningful privacy choices in their systems. We also present a use case of how we leverage the design space to finalize the design decisions for a real-world privacy choice platform, the Internet of Things (IoT) Assistant, to provide meaningful privacy control in the IoT.
Authors:
; ;
Award ID(s):
1914486 1914444 1914446
Publication Date:
NSF-PAR ID:
10257032
Journal Name:
Proceedings of the 2021 CHI Conference on Human Factors in computing Systems
Page Range or eLocation-ID:
1 to 16
Sponsoring Org:
National Science Foundation
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