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dc.contributor.advisorHenderson, Tristan
dc.contributor.advisorYe, Juan
dc.contributor.authorZhao, Yuchen
dc.coverage.spatialvi, 131 p.en_US
dc.description.abstractLocation-sharing services have become increasingly popular with the proliferation of smartphones and online social networks. People share their locations with each other to record their daily lives or satisfy their social needs. At the same time, inappropriate disclosure of location information poses threats to people's privacy. One of the reasons why people fail to protect their location privacy is the difficulty of using the current mechanisms to manually configure location-privacy settings. Since people's location-privacy preferences are context-aware, manual configuration is cumbersome. People's incapability and unwillingness to do so lead to unexpected location disclosures that violate their location privacy. In this thesis, we investigate the feasibility of using recommender systems to help people protect their location privacy. We examine the performance of location-privacy recommender systems and compare it with the state-of-the-art. We also conduct online user studies to understand people's acceptance of such recommender systems and their concerns. We revise our design of the systems according to the results of the user studies. We find that user-based collaborative filtering can accurately recommend location-privacy preferences and outperform the state-of-the-art when training data are insufficient. From users' perspective, their acceptance of location-privacy recommender systems is affected by the openness and the context of recommendations and their privacy concerns about the systems. It is feasible to use data obfuscation or decentralisation to alleviate people's concerns and meanwhile keep the systems robust against malicious data attacks.en_US
dc.publisherUniversity of St Andrews
dc.subjectLocation-based servicesen_US
dc.subjectLocation-sharing servicesen_US
dc.subjectRecommender systemsen_US
dc.subjectUser studiesen_US
dc.subjectPrivacy preferencesen_US
dc.subjectUser acceptanceen_US
dc.subjectOpportunistic networksen_US
dc.subjectShilling attacken_US
dc.subjectReputation systemsen_US
dc.titleRecommending privacy preferences in location-sharing servicesen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US

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