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dc.contributor.authorZhao, Yuchen
dc.contributor.authorYe, Juan
dc.contributor.authorHenderson, Tristan
dc.date.accessioned2016-10-26T14:30:15Z
dc.date.available2016-10-26T14:30:15Z
dc.date.issued2016-12-01
dc.identifier.citationZhao , Y , Ye , J & Henderson , T 2016 , A robust reputation-based location-privacy recommender system using opportunistic networks . in Proceedings of The 8th EAI International Conference on Mobile Computing, Applications and Services . ACM , 8th EAI International Conference on Mobile Computing, Applications and Services , Cambridge , United Kingdom , 30/11/16 . https://doi.org/10.4108/eai.30-11-2016.2267031en
dc.identifier.citationconferenceen
dc.identifier.otherPURE: 245970023
dc.identifier.otherPURE UUID: 2c1d6bcf-2a6e-41f5-b6da-b7b4f3af41ee
dc.identifier.otherBibtex: urn:f11eacd7e054cf8fc0de23895049c6d1
dc.identifier.otherScopus: 85041291091
dc.identifier.urihttp://hdl.handle.net/10023/9707
dc.description.abstractLocation-sharing services have grown in use commensurately with the increasing popularity of smart phones. As location data can be sensitive, it is important to preserve people’s privacy while using such services, and so location-privacy recommender systems have been proposed to help people configure their privacy settings.These recommenders collect and store people’s data in a centralised system, but these themselves can introduce new privacy threats and concerns.In this paper, we propose a decentralised location-privacy recommender system based on opportunistic networks. We evaluate our system using real-world location-privacy traces, and introduce a reputation scheme based on encounter frequencies to mitigate the potential effects of shilling attacks by malicious users. Experimental results show that, after receiving adequate data, our decentralised recommender system’s performance is close to the performance of traditional centralised recommender systems (3% difference in accuracy and 1% difference in leaks). Meanwhile, our reputation scheme significantly mitigates the effect of malicious users’input (from 55% to 8% success) and makes it increasingly expensive to conduct such attacks.
dc.language.isoeng
dc.publisherACM
dc.relation.ispartofProceedings of The 8th EAI International Conference on Mobile Computing, Applications and Servicesen
dc.rightsCopyright 2016, EAI. This work has been made available online in accordance with the publisher's policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at www.eudl.eu / http://dx.doi.org/10.4108/eai.30-11-2016.2267031en
dc.subjectLocation-based servicesen
dc.subjectPrivacyen
dc.subjectRecommender systemsen
dc.subjectOpportunistic networksen
dc.subjectSecurityen
dc.subjectShilling attacken
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectNDASen
dc.subject.lccQA75en
dc.titleA robust reputation-based location-privacy recommender system using opportunistic networksen
dc.typeConference itemen
dc.description.versionPreprinten
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.4108/eai.30-11-2016.2267031
dc.identifier.urlhttp://mobicase.org/2016/


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