Location Sharing Privacy Preference: Analysis and Personalized Recommendation. Xie, J., Knijnenburg, B. P., & Jin, H. In Proceedings of the 19th International Conference on Intelligent User Interfaces, of IUI '14, pages 189--198, New York, NY, USA, 2014. ACM. 00000![Location Sharing Privacy Preference: Analysis and Personalized Recommendation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{xie_location_2014,
address = {New York, NY, USA},
series = {{IUI} '14},
title = {Location {Sharing} {Privacy} {Preference}: {Analysis} and {Personalized} {Recommendation}},
isbn = {978-1-4503-2184-6},
shorttitle = {Location {Sharing} {Privacy} {Preference}},
url = {http://doi.acm.org/10.1145/2557500.2557504},
doi = {10.1145/2557500.2557504},
abstract = {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.},
urldate = {2014-05-19TZ},
booktitle = {Proceedings of the 19th {International} {Conference} on {Intelligent} {User} {Interfaces}},
publisher = {ACM},
author = {Xie, Jierui and Knijnenburg, Bart Piet and Jin, Hongxia},
year = {2014},
note = {00000},
pages = {189--198}
}
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