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dc.contributor.authorWang, Weiqi
dc.contributor.authorLiu, An
dc.contributor.authorLi, Zhixu
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorLi, Qing
dc.contributor.authorZhou, Xiaofang
dc.date.accessioned2018-04-10T10:23:33Z
dc.date.available2018-04-10T10:23:33Z
dc.date.issued2018-04-04
dc.identifier.citationWang W, Liu A, Li Z, Zhang X, Li Q, et al. (2018) Protecting multi-party privacy in location-aware social point-of-interest recommendation. World Wide Web. Available: http://dx.doi.org/10.1007/s11280-018-0550-9.
dc.identifier.issn1386-145X
dc.identifier.issn1573-1413
dc.identifier.doi10.1007/s11280-018-0550-9
dc.identifier.urihttp://hdl.handle.net/10754/627428
dc.description.abstractPoint-of-interest (POI) recommendation has attracted much interest recently because of its significant business potential. Data used in POI recommendation (e.g., user-location check-in matrix) are much more sparse than that used in traditional item (e.g., book and movie) recommendation, which leads to more serious cold start problem. Social POI recommendation has proved to be an effective solution, but most existing works assume that recommenders have access to all required data. This is very rare in practice because these data are generally owned by different entities who are not willing to share their data with others due to privacy and legal concerns. In this paper, we first propose PLAS, a protocol which enables effective POI recommendation without disclosing the sensitive data of every party getting involved in the recommendation. We formally show PLAS is secure in the semi-honest adversary model. To improve its performance. We then adopt the technique of cloaking area by which expensive distance computation over encrypted data is replaced by cheap operation over plaintext. In addition, we utilize the sparsity of check-ins to selectively publish data, thus reducing encryption cost and avoiding unnecessary computation over ciphertext. Experiments on two real datasets show that our protocol is feasible and can scale to large POI recommendation problems in practice.
dc.description.sponsorshipResearch reported in this publication was partially supported Natural Science Foundation of China (Grant Nos. 61572336, 61572335, 61402313)
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/article/10.1007/s11280-018-0550-9
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s11280-018-0550-9
dc.subjectLocation privacy
dc.subjectPoint-of-interest
dc.subjectRecommendation
dc.titleProtecting multi-party privacy in location-aware social point-of-interest recommendation
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalWorld Wide Web
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Computer Science and Technology, Soochow University, Suzhou, China
dc.contributor.institutionDepartment of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong
dc.contributor.institutionSchool of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
kaust.personZhang, Xiangliang
refterms.dateFOA2019-04-04T00:00:00Z
dc.date.published-online2018-04-04
dc.date.published-print2019-03


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