The active badge location system
ACM Transactions on Information Systems (TOIS)
C4.5: programs for machine learning
C4.5: programs for machine learning
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Who wants to know what when? privacy preference determinants in ubiquitous computing
CHI '03 Extended Abstracts on Human Factors in Computing Systems
User needs for location-aware mobile services
Personal and Ubiquitous Computing
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Designing example-critiquing interaction
Proceedings of the 9th international conference on Intelligent user interfaces
An architecture for privacy-sensitive ubiquitous computing
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Location disclosure to social relations: why, when, & what people want to share
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Context-aware telephony: privacy preferences and sharing patterns
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Proceedings of the 6th international conference on Mobile and ubiquitous multimedia
User-controllable learning of security and privacy policies
Proceedings of the 1st ACM workshop on Workshop on AISec
Understanding and capturing people's privacy policies in a mobile social networking application
Personal and Ubiquitous Computing
Privacy in mobile technology for personal healthcare
ACM Computing Surveys (CSUR)
When privacy and utility are in harmony: towards better design of presence technologies
Personal and Ubiquitous Computing
Disclosure Intention of Location-Related Information in Location-Based Social Network Services
International Journal of Electronic Commerce
A comparative study of location-sharing privacy preferences in the United States and China
Personal and Ubiquitous Computing
Privacy manipulation and acclimation in a location sharing application
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Location sharing privacy preference: analysis and personalized recommendation
Proceedings of the 19th international conference on Intelligent User Interfaces
Reconciling mobile app privacy and usability on smartphones: could user privacy profiles help?
Proceedings of the 23rd international conference on World wide web
Electronic Commerce Research
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Social networking sites such as Facebook and MySpace thrive on the exchange of personal content such as pictures and activities. These sites are discovering that people's privacy preferences are very rich and diverse. In theory, providing users with more expressive settings to specify their privacy policies would not only enable them to better articulate their preferences, but could also lead to greater user burden. In this article, we evaluate to what extent providing users with default policies can help alleviate some of this burden. Our research is conducted in the context of location-sharing applications, where users are expected to specify conditions under which they are willing to let others see their locations. We define canonical policies that attempt to abstract away user-specific elements such as a user's default schedule, or canonical places, such as "work" and "home." We learn a set of default policies from this data using decision-tree and clustering algorithms. We examine trade-offs between the complexity / understandability of default policies made available to users, and the accuracy with which they capture the ground truth preferences of our user population. Specifically, we present results obtained using data collected from 30 users of location-enabled phones over a period of one week. They suggest that providing users with a small number of canonical default policies to choose from can help reduce user burden when it comes to customizing the rich privacy settings they seem to require.