Show simple item record

Files in this item


Item metadata

dc.contributor.authorZhao, Yuchen
dc.contributor.authorYe, Juan
dc.contributor.authorHenderson, Tristan
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.otherPURE: 130423612
dc.identifier.otherPURE UUID: ea065250-8ffd-4575-80e1-5c8a58479d07
dc.identifier.otherORCID: /0000-0002-2838-6836/work/68280974
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.rights© 2014 The Authors.en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titleRecommending Location Privacy Preferences in Ubiquitous Computingen
dc.typeConference posteren
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.description.statusPeer revieweden

This item appears in the following Collection(s)

Show simple item record