Unpacking "privacy" for a networked world
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Internet Users' Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model
Information Systems Research
From awareness to repartee: sharing location within social groups
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A survey of computational location privacy
Personal and Ubiquitous Computing
Understanding and capturing people's privacy policies in a mobile social networking application
Personal and Ubiquitous Computing
The Circles of Latitude: Adoption and Usage of Location Tracking in Online Social Networking
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Generating default privacy policies for online social networks
CHI '10 Extended Abstracts on Human Factors in Computing Systems
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Researchers in the area of privacy management often suggest to provide users with a collection of privacy settings and good defaults for them. However, our research into people's attitudes towards location-sharing technology (considering both adopters and non-adopters) indicates that the right way to manage privacy and the right default can vary for different types of people; Key privacy concerns may differ by demographics and personality type, and personality may also influence privacy management preferences. To help researchers and practitioners better understand who is concerned about what, and how to best address those concerns, we will draw on our research and theories in the literature to construct and validate a scale that 1) assesses an individual's main privacy concerns towards location-sharing technology, and 2) measures personality traits relevant to privacy management. We will then put this scale into practice by deploying an enterprise-wide survey at our field site (a large multi-national entertainment corporation) that tests the relationship between the scale/subscales and an individual's intention to adopt location-sharing technology. We hope this will help us identify subpopulations with similar privacy concerns and/or personality traits, which can guide future design of privacy-sensitive location-sharing technology.