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dc.contributor.authorShaham, Sina
dc.contributor.authorDing, Ming
dc.contributor.authorLiu, Bo
dc.contributor.authorDang, Shuping
dc.contributor.authorLin, Zihuai
dc.contributor.authorLi, Jun
dc.date.accessioned2020-05-13T09:18:43Z
dc.date.available2020-05-13T09:18:43Z
dc.date.issued2020-05-12
dc.identifier.citationShaham, S., Ding, M., Liu, B., Dang, S., Lin, Z., & Li, J. (2020). Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model. IEEE Transactions on Mobile Computing, 1–1. doi:10.1109/tmc.2020.2993599
dc.identifier.issn2161-9875
dc.identifier.doi10.1109/TMC.2020.2993599
dc.identifier.urihttp://hdl.handle.net/10754/662822
dc.description.abstractRecent years have seen rising needs for location-based services in our everyday life. Aside from the many advantages provided by these services, they have caused serious concerns regarding the location privacy of users. Adversaries can monitor the queried locations by users to infer sensitive information, such as home addresses and shopping habits. To address this issue, dummy-based algorithms have been developed to increase the anonymity of users, and thus, protecting their privacy. Unfortunately, the existing algorithms only assume a limited amount of side information known by adversaries, which may face more severe challenges in practice. In this paper, we develop an attack model termed as Viterbi attack, which represents a realistic privacy threat on user trajectories. Moreover, we propose a metric called transition entropy that enables the evaluation of dummy-based algorithms, followed by developing a robust algorithm that can defend users against the Viterbi attack while maintaining significantly high performance in terms of the traditional metrics. We compare and evaluate our proposed algorithm and metric on a publicly available dataset published by Microsoft, i.e., Geolife dataset.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9090973/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9090973
dc.relation.urlhttp://arxiv.org/pdf/1805.06104
dc.rights(c) 2020 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.
dc.subjectk-anonymity
dc.subjectspatio-temporal trajectories
dc.subjectlocation-based services
dc.subjectprivacy preservation
dc.titlePrivacy Preservation in Location-Based Services: A Novel Metric and Attack Model
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalIEEE Transactions on Mobile Computing
dc.eprint.versionPost-print
dc.contributor.institutionEngineering and IT, The university of sydney, Sydney, New South Wales Australia
dc.contributor.institutionNetworks Group, Data61, 170512 Eveleigh, New South Wales Australia
dc.contributor.institutionEngineering and IT, University of Technology Sydney, 1994 Ultimo, New South Wales Australia
dc.contributor.institutionSchool of Electrical and Information Engineering, University of Sydney, NEWTOWN, New South Wales Australia
dc.contributor.institutionSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, 12436 Nanjing, Jiangsu China
dc.identifier.pages1-1
dc.identifier.arxivid1805.06104
kaust.personDang, Shuping
refterms.dateFOA2020-05-13T10:42:09Z
dc.date.published-online2020-05-12
dc.date.published-print2021-10-01


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