Tailoring Privacy to Users' Needs
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Testing the significance of attribute interactions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Location disclosure to social relations: why, when, & what people want to share
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Who gets to know what when: configuring privacy permissions in an awareness application
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Information revelation and privacy in online social networks
Proceedings of the 2005 ACM workshop on Privacy in the electronic society
From awareness to repartee: sharing location within social groups
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Understanding privacy settings in facebook with an audience view
UPSEC'08 Proceedings of the 1st Conference on Usability, Psychology, and Security
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Strategies and struggles with privacy in an online social networking community
BCS-HCI '08 Proceedings of the 22nd British HCI Group Annual Conference on People and Computers: Culture, Creativity, Interaction - Volume 1
Privacy stories: confidence in privacy behaviors through end user programming
Proceedings of the 5th Symposium on Usable Privacy and Security
The impact of expressiveness on the effectiveness of privacy mechanisms for location-sharing
Proceedings of the 5th Symposium on Usable Privacy and Security
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Capturing Social Networking Privacy Preferences
PETS '09 Proceedings of the 9th International Symposium on Privacy Enhancing Technologies
Generating default privacy policies for online social networks
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Privacy wizards for social networking sites
Proceedings of the 19th international conference on World wide web
Empirical models of privacy in location sharing
Proceedings of the 12th ACM international conference on Ubiquitous computing
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Understanding choice overload in recommender systems
Proceedings of the fourth ACM conference on Recommender systems
On the semantic annotation of places in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs
Personal and Ubiquitous Computing
Imagined communities: awareness, information sharing, and privacy on the facebook
PET'06 Proceedings of the 6th international conference on Privacy Enhancing Technologies
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Preference-based location sharing: are more privacy options really better?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Privacy manipulation and acclimation in a location sharing application
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
FYI: communication style preferences underlie differences in location-sharing adoption and usage
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
Dimensionality of information disclosure behavior
International Journal of Human-Computer Studies
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Location-based systems are becoming more popular with the explosive growth in popularity of smart phones. However, the user adoption of these systems is hindered by growing user concerns about privacy. To design better location-based systems that attract more user adoption and protect users from information under/overexposure, it is highly desirable to understand users' location sharing and privacy preferences. This paper makes two main contributions. First, by studying users' location sharing privacy preferences with three groups of people (i.e., Family, Friend and Colleague) in different contexts, including check-in time, companion and emotion, we reveal that location sharing behaviors are highly dynamic, context-aware, audience-aware and personal. In particular, we find that emotion and companion are good contextual predictors of privacy preferences. Moreover, we find that there are strong similarities or correlations among contexts and groups. Our second contribution is to show, in light of the user study, that despite the dynamic and context-dependent nature of location sharing, it is still possible to predict a user's in-situ sharing preference in various contexts. More specifically, we explore whether it is possible to give users a personalized recommendation of the sharing setting they are most likely to prefer, based on context similarity, group correlation and collective check-in preference. PPRec, the proposed recommendation algorithm that incorporates the above three elements, delivers personalized recommendations that could be helpful to reduce both user's burden and privacy risk. It also provides additional insights into the relative usefulness of different personal and contextual factors in predicting users' sharing behavior.