Proceedings Article10.1145/1572532.1572538
A "nutrition label" for privacy
Patrick Gage Kelley,Joanna Bresee,Lorrie Faith Cranor,Robert W. Reeder +3 more
- 15 Jul 2009
- pp 4
TL;DR: The study results demonstrate that compared to existing natural language privacy policies, the proposed privacy label allows participants to find information more quickly and accurately, and provides a more enjoyable information seeking experience.
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Abstract: We used an iterative design process to develop a privacy label that presents to consumers the ways organizations collect, use, and share personal information. Many surveys have shown that consumers are concerned about online privacy, yet current mechanisms to present website privacy policies have not been successful. This research addresses the present gap in the communication and understanding of privacy policies, by creating an information design that improves the visual presentation and comprehensibility of privacy policies. Drawing from nutrition, warning, and energy labeling, as well as from the effort towards creating a standardized banking privacy notification, we present our process for constructing and refining a label tuned to privacy. This paper describes our design methodology; findings from two focus groups; and accuracy, timing, and likeability results from a laboratory study with 24 participants. Our study results demonstrate that compared to existing natural language privacy policies, the proposed privacy label allows participants to find information more quickly and accurately, and provides a more enjoyable information seeking experience.
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Citations
"Why is Everything in the Cloud?": Co-Designing Visual Cues Representing Data Processes with Children
Kaiwen Sun,Ritesh Kanchi,Frances Marie Tabio Ello,Li-Neishin Co,Ming‐Ting Wu,Susan A. Gelman,Jenny Radesky,Florian Schaub,Jason Yip +8 more
- 17 Jun 2024
TL;DR: Children struggle to understand data processes and privacy implications due to vague or absent visual cues. We designed and iteratively tested new visual cues to improve understanding and provide children with more control over their data.
PolicyLR: A Logic Representation For Privacy Policies
Ashish Hooda,Rishabh Khandelwal,P. Chalasani,Kassem Fawaz,Somesh Jha +4 more
TL;DR: PolicyLR proposes a machine-readable representation of privacy policies, using valuations of atomic formulae to facilitate compliance, consistency, and other tasks, achieving 0.91 precision and 0.88 recall on a community-annotated dataset with open-source Large Language Models.
PrivacyCube: Data Physicalization for Enhancing Privacy Awareness in IoT
Bayan Al Muhander,Nalin Asanka Gamagedara Arachchilage,Charith Perera,Mohammed F. Alosaimi,Omer Rana,Charith Perera +5 more
- 08 Jun 2024
TL;DR: PrivacyCube is a data physicalization designed to enhance privacy awareness in IoT environments. It visualizes IoT data consumption and encourages conversations about data management practices. PrivacyCube has been shown to significantly increase privacy awareness and is preferred over text privacy policies.
The Privacy Implications of Cyber Security Systems: A Technological Survey
Eran Toch,Claudio Bettini,Erez Shmueli,Laura Radaelli,Andrea Lanzi,Daniele Riboni,Bruno Lepri +6 more
TL;DR: A taxonomy for privacy risks assessment of information security technologies is suggested, based on thelevel of data exposure, the level of identification of individual users, the data sensitivity and the user control over the monitoring, and collection and analysis of the data.
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