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Recommending Location Privacy Preferences in Ubiquitous Computing
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dc.contributor.author | Zhao, Yuchen | |
dc.contributor.author | Ye, Juan | |
dc.contributor.author | Henderson, Tristan | |
dc.date.accessioned | 2014-07-28T11:31:01Z | |
dc.date.available | 2014-07-28T11:31:01Z | |
dc.date.issued | 2014-07-23 | |
dc.identifier.citation | Zhao , 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.citation | conference | en |
dc.identifier.other | PURE: 130423612 | |
dc.identifier.other | PURE UUID: ea065250-8ffd-4575-80e1-5c8a58479d07 | |
dc.identifier.other | ORCID: /0000-0002-2838-6836/work/68280974 | |
dc.identifier.uri | https://hdl.handle.net/10023/5075 | |
dc.description.abstract | Location-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.iso | eng | |
dc.rights | © 2014 The Authors. | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject.lcc | QA75 | en |
dc.title | Recommending Location Privacy Preferences in Ubiquitous Computing | en |
dc.type | Conference poster | en |
dc.description.version | Postprint | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.description.status | Peer reviewed | en |
dc.identifier.url | http://www.sigsac.org/wisec/WiSec2014/ | en |
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