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dc.contributor.authorZhao, Yuchen
dc.contributor.authorYe, Juan
dc.contributor.authorHenderson, Tristan
dc.date.accessioned2014-07-28T11:31:01Z
dc.date.available2014-07-28T11:31:01Z
dc.date.issued2014-07-23
dc.identifier.citationZhao , Y , Ye , J & Henderson , T 2014 , ' Recommending Location Privacy Preferences in Ubiquitous Computing ' , 7th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) , Oxford , United Kingdom , 23/07/14 - 25/07/14 .en
dc.identifier.citationconferenceen
dc.identifier.otherPURE: 130423612
dc.identifier.otherPURE UUID: ea065250-8ffd-4575-80e1-5c8a58479d07
dc.identifier.otherORCID: /0000-0002-2838-6836/work/68280974
dc.identifier.urihttp://hdl.handle.net/10023/5075
dc.description.abstractLocation-Based Services have become increasingly popular due to the prevalence of smart devices. The protection of users’ location privacy in such systems is a vital issue. Conventional privacy protection methods such as manually predefining privacy rules or asking users to make decisions every time they enter a new location may not be usable, and so researchers have explored the use of machine learning to predict preferences. Model-based machine learning classifiers which are used for prediction may be too computationally complex to be used in real-world applications. We propose a location-privacy recommender that can provide users with recommendations of appropriate location privacy settings through user-user collaborative filtering. We test our scheme on real world dataset and the experiment results show that the performance of our scheme is close to the best performance of model-based classifiers and it outperforms model-based classifiers when there are no sufficient training data.
dc.language.isoeng
dc.rights© 2014 The Authors.en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subject.lccQA75en
dc.titleRecommending Location Privacy Preferences in Ubiquitous Computingen
dc.typeConference posteren
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.description.statusPeer revieweden
dc.identifier.urlhttp://www.sigsac.org/wisec/WiSec2014/en


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