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dc.contributor.authorLiu, Xiao
dc.contributor.authorLiu, An
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorLi, Zhixu
dc.contributor.authorLiu, Guanfeng
dc.contributor.authorZhao, Lei
dc.contributor.authorZhou, Xiaofang
dc.date.accessioned2017-05-31T11:23:14Z
dc.date.available2017-05-31T11:23:14Z
dc.date.issued2017-03-22
dc.identifier.citationLiu X, Liu A, Zhang X, Li Z, Liu G, et al. (2017) When Differential Privacy Meets Randomized Perturbation: A Hybrid Approach for Privacy-Preserving Recommender System. Lecture Notes in Computer Science: 576–591. Available: http://dx.doi.org/10.1007/978-3-319-55753-3_36.
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.doi10.1007/978-3-319-55753-3_36
dc.identifier.urihttp://hdl.handle.net/10754/623935
dc.description.abstractPrivacy risks of recommender systems have caused increasing attention. Users’ private data is often collected by probably untrusted recommender system in order to provide high-quality recommendation. Meanwhile, malicious attackers may utilize recommendation results to make inferences about other users’ private data. Existing approaches focus either on keeping users’ private data protected during recommendation computation or on preventing the inference of any single user’s data from the recommendation result. However, none is designed for both hiding users’ private data and preventing privacy inference. To achieve this goal, we propose in this paper a hybrid approach for privacy-preserving recommender systems by combining differential privacy (DP) with randomized perturbation (RP). We theoretically show the noise added by RP has limited effect on recommendation accuracy and the noise added by DP can be well controlled based on the sensitivity analysis of functions on the perturbed data. Extensive experiments on three large-scale real world datasets show that the hybrid approach generally provides more privacy protection with acceptable recommendation accuracy loss, and surprisingly sometimes achieves better privacy without sacrificing accuracy, thus validating its feasibility in practice.
dc.description.sponsorshipThis work was done while the first author was a visiting student at King Abdullah University of Science and Technology (KAUST). Research reported in this publication was partially supported by KAUST and Natural Science Foundation of China (Grant Nos. 61572336, 61572335, 61632016, 61402313).
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/chapter/10.1007/978-3-319-55753-3_36
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-55753-3_36
dc.subjectRecommender systems
dc.subjectPrivacy-preserving
dc.subjectDifferential privacy
dc.subjectRandomized perturbation
dc.titleWhen Differential Privacy Meets Randomized Perturbation: A Hybrid Approach for Privacy-Preserving Recommender System
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalDatabase Systems for Advanced Applications
dc.conference.date2017-03-27 to 2017-03-30
dc.conference.name22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017
dc.conference.locationSuzhou, CHN
dc.eprint.versionPost-print
dc.contributor.institutionSoochow University, Suzhou, China
dc.contributor.institutionUniversity of Queensland, Brisbane, Australia
kaust.personLiu, An
kaust.personZhang, Xiangliang
refterms.dateFOA2018-03-22T00:00:00Z
dc.date.published-online2017-03-22
dc.date.published-print2017


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