Privacy-aware Location Privacy Preference Recommendations. Zhao, Y., Ye, J., & Henderson, T. In MOBIQUITOUS '14, pages 120–129, ICST, Brussels, Belgium, Belgium, 2014. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering). Journal Abbreviation: MOBIQUITOUS '14![Privacy-aware Location Privacy Preference Recommendations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Location-Based Services have become increasingly popular due to the prevalence of smart devices and location-sharing applications such as Facebook and Foursquare. The protection of people's sensitive location data in such applications is an important requirement. Conventional location privacy protection methods, however, such as manually defining privacy rules or asking users to make decisions each time they enter a new location may be overly complex, intrusive or unwieldy. An alternative is to use machine learning to predict people's privacy preferences and automatically configure settings. Model-based machine learning classifiers may be too computationally complex to be used in real-world applications, or suffer from poor performance when training data are insufficient. In this paper we propose a location-privacy recommender that can provide people with recommendations of appropriate location privacy settings through user-user collaborative filtering. Using a real-world location-sharing dataset, we show that the prediction accuracy of our scheme (73.08%) is similar to the best performance of model-based classifiers (75.30%), and at the same time causes fewer privacy leaks (11.75% vs 12.70%). Our scheme further outperforms model-based classifiers when there are insufficient training data. Since privacy preferences are innately private, we make our recommender privacy-aware by obfuscating people's preferences. Our results show that obfuscation leads to a minimal loss of prediction accuracy (0.76%).
@inproceedings{zhao_privacy-aware_2014,
address = {ICST, Brussels, Belgium, Belgium},
title = {Privacy-aware {Location} {Privacy} {Preference} {Recommendations}},
url = {http://dx.doi.org/10.4108/icst.mobiquitous.2014.258017},
doi = {10.4108/icst.mobiquitous.2014.258017},
abstract = {Location-Based Services have become increasingly popular due to the
prevalence of smart devices and location-sharing applications such as
Facebook and Foursquare. The protection of people's sensitive location
data in such applications is an important requirement. Conventional
location privacy protection methods, however, such as manually defining
privacy rules or asking users to make decisions each time they enter a new
location may be overly complex, intrusive or unwieldy. An alternative is
to use machine learning to predict people's privacy preferences and
automatically configure settings. Model-based machine learning classifiers
may be too computationally complex to be used in real-world applications,
or suffer from poor performance when training data are insufficient. In
this paper we propose a location-privacy recommender that can provide
people with recommendations of appropriate location privacy settings
through user-user collaborative filtering. Using a real-world
location-sharing dataset, we show that the prediction accuracy of our
scheme (73.08\%) is similar to the best performance of model-based
classifiers (75.30\%), and at the same time causes fewer privacy leaks
(11.75\% vs 12.70\%). Our scheme further outperforms model-based classifiers
when there are insufficient training data. Since privacy preferences are
innately private, we make our recommender privacy-aware by obfuscating
people's preferences. Our results show that obfuscation leads to a minimal
loss of prediction accuracy (0.76\%).},
urldate = {2015-09-23},
booktitle = {{MOBIQUITOUS} '14},
publisher = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)},
author = {Zhao, Yuchen and Ye, Juan and Henderson, Tristan},
year = {2014},
note = {Journal Abbreviation: MOBIQUITOUS '14},
pages = {120--129},
}
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An alternative is to use machine learning to predict people's privacy preferences and automatically configure settings. Model-based machine learning classifiers may be too computationally complex to be used in real-world applications, or suffer from poor performance when training data are insufficient. In this paper we propose a location-privacy recommender that can provide people with recommendations of appropriate location privacy settings through user-user collaborative filtering. Using a real-world location-sharing dataset, we show that the prediction accuracy of our scheme (73.08%) is similar to the best performance of model-based classifiers (75.30%), and at the same time causes fewer privacy leaks (11.75% vs 12.70%). Our scheme further outperforms model-based classifiers when there are insufficient training data. Since privacy preferences are innately private, we make our recommender privacy-aware by obfuscating people's preferences. 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