A Privacy-Preserving Framework for Trust-Oriented Point-of-Interest Recommendation

Handle URI:
http://hdl.handle.net/10754/625939
Title:
A Privacy-Preserving Framework for Trust-Oriented Point-of-Interest Recommendation
Authors:
Liu, An; Wang, Weiqi; Li, Zhixu; Liu, Guanfeng; Li, Qing; Zhou, Xiaofang; Zhang, Xiangliang ( 0000-0002-3574-5665 )
Abstract:
Point-of-Interest (POI) recommendation has attracted many interests recently because of its significant potential for helping users to explore new places and helping LBS providers to carry out precision marketing. Compared with the user-item rating matrix in conventional recommender systems, the user-location check-in matrix in POI recommendation is usually much more sparse, which makes the notorious cold start problem more prominent in POI recommendation. Trust-oriented recommendation is an effective way to deal with this problem but it requires that the recommender has access to user check-in and trust data. In practice, however, these data are usually owned by different businesses who are not willing to share their data with the recommender mainly due to privacy and legal concerns. In this paper, we propose a privacy-preserving framework to boost data owners willingness to share their data with untrustworthy businesses. More specifically, we utilize partially homomorphic encryption to design two protocols for privacy-preserving trustoriented POI recommendation. By offline encryption and parallel computing, these protocols can efficiently protect the private data of every party involved in the recommendation. We prove that the proposed protocols are secure against semi-honest adversaries. Experiments on both synthetic data and real data show that our protocols can achieve privacy-preserving with acceptable computation and communication cost.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Citation:
Liu A, Wang W, Li Z, Liu G, Li Q, et al. (2017) A Privacy-Preserving Framework for Trust-Oriented Point-of-Interest Recommendation. IEEE Access: 1–1. Available: http://dx.doi.org/10.1109/ACCESS.2017.2765317.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Access
Issue Date:
23-Oct-2017
DOI:
10.1109/ACCESS.2017.2765317
Type:
Article
ISSN:
2169-3536
Sponsors:
The work described in this paper was partially supported by KAUST and the National Natural Science Foundation of China (No. 61572336, No. 61402313, No. 61702227, No. 61572335, and No.61472337).
Additional Links:
http://ieeexplore.ieee.org/document/8078176/
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLiu, Anen
dc.contributor.authorWang, Weiqien
dc.contributor.authorLi, Zhixuen
dc.contributor.authorLiu, Guanfengen
dc.contributor.authorLi, Qingen
dc.contributor.authorZhou, Xiaofangen
dc.contributor.authorZhang, Xiangliangen
dc.date.accessioned2017-10-25T08:36:18Z-
dc.date.available2017-10-25T08:36:18Z-
dc.date.issued2017-10-23en
dc.identifier.citationLiu A, Wang W, Li Z, Liu G, Li Q, et al. (2017) A Privacy-Preserving Framework for Trust-Oriented Point-of-Interest Recommendation. IEEE Access: 1–1. Available: http://dx.doi.org/10.1109/ACCESS.2017.2765317.en
dc.identifier.issn2169-3536en
dc.identifier.doi10.1109/ACCESS.2017.2765317en
dc.identifier.urihttp://hdl.handle.net/10754/625939-
dc.description.abstractPoint-of-Interest (POI) recommendation has attracted many interests recently because of its significant potential for helping users to explore new places and helping LBS providers to carry out precision marketing. Compared with the user-item rating matrix in conventional recommender systems, the user-location check-in matrix in POI recommendation is usually much more sparse, which makes the notorious cold start problem more prominent in POI recommendation. Trust-oriented recommendation is an effective way to deal with this problem but it requires that the recommender has access to user check-in and trust data. In practice, however, these data are usually owned by different businesses who are not willing to share their data with the recommender mainly due to privacy and legal concerns. In this paper, we propose a privacy-preserving framework to boost data owners willingness to share their data with untrustworthy businesses. More specifically, we utilize partially homomorphic encryption to design two protocols for privacy-preserving trustoriented POI recommendation. By offline encryption and parallel computing, these protocols can efficiently protect the private data of every party involved in the recommendation. We prove that the proposed protocols are secure against semi-honest adversaries. Experiments on both synthetic data and real data show that our protocols can achieve privacy-preserving with acceptable computation and communication cost.en
dc.description.sponsorshipThe work described in this paper was partially supported by KAUST and the National Natural Science Foundation of China (No. 61572336, No. 61402313, No. 61702227, No. 61572335, and No.61472337).en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/8078176/en
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectData privacyen
dc.subjectEncryptionen
dc.subjectPrivacyen
dc.subjectProtocolsen
dc.subjectRecommender systemsen
dc.subjectSocial network servicesen
dc.subjectSparse matricesen
dc.titleA Privacy-Preserving Framework for Trust-Oriented Point-of-Interest Recommendationen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalIEEE Accessen
dc.eprint.versionPost-printen
dc.contributor.institutionSchool of Computer Science and Technology, Soochow University, Suzhou, Chinaen
dc.contributor.institutiondepartment of Computer Science, City University of Hong Kong, Hong Kong, Chinaen
dc.contributor.institutionSchool of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australiaen
kaust.authorLiu, Anen
kaust.authorZhang, Xiangliangen
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