A "nutrition label" for privacy
By Patrick Gage Kelley, Joanna Bresee, Lorrie Faith Cranor, R. W. Reeder. Symposium On Usable Privacy and Security, 2009
301 citations recorded by the source corpus.
Subfields: Clinical Psychology, Communication, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/fd0de435e4d3795bd9cb92d427e02eefff635e1c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Clinical Psychology
In Communication
In Sociology and Political Science
- Norman M. Sadeh
- Jason I. Hong
- Lorrie Faith Cranor
- Florian Schaub
- Jialiu Lin
- Yuvraj Agarwal
- Joshua Gluck
- Hana Habib
Citations in the corpus
- How Short Is Too Short? Implications of Length and Framing on the Effectiveness of Privacy Notices (2016, methodology): “Here we discuss prior work on privacy notice design in three areas: short-form privacy notices, framing, and delivery methods.”
- A Conundrum of Permissions: Installing Applications on an Android Smartphone (2012, background): “Research in privacy policies, financial privacy notices, and access control have all similarly shown that privacy-related concepts and terms are often not well understood by users expected to make privacy decisions [9,10,14].”
- Six Human-Centered Artificial Intelligence Grand Challenges (2023, unknown): “Nutrition-like product labels for security and privacy have been proposed (Kelley et al., 2009) and proto-typed (Emami-Naeini et al., 2020), and the use of Foundation for Responsible Robotics Quality Marks of responsible practices for AI-enabled robotics is currently being piloted (Foundation for…”
- The Illusion of Control: Placebo Effects of Control Settings (2018, background): “Finally, despite the prevalence of control settings on social media, studies of how people are able to find them and how likely they are to use them have primarily occurred within the narrow domain of privacy controls [4, 26, 28, 61, 65].”
- Expectation and purpose: understanding users' mental models of mobile app privacy through crowdsourcing (2012, background): “Our work extends this past work to a new domain, namely mobile app privacy.”
- Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning (2018, background): “Users: Polisis can automatically populate several of the previously-proposed short notices for privacy policies, such as nutrition tables and privacy icons [3, 18, 31, 32].”