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Location proximity attacks against mobile targets: Analytical bounds and attacker strategies

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dc.creatorWang X.spa
dc.creatorHou X.spa
dc.creatorRios R.spa
dc.creatorHallgren P.spa
dc.creatorTippenhauer N.O.spa
dc.creatorOchoa M.spa
dc.date.accessioned2020-05-25T23:56:48Z
dc.date.available2020-05-25T23:56:48Z
dc.date.created2018spa
dc.description.abstractLocation privacy has mostly focused on scenarios where users remain static. However, investigating scenarios where the victims present a particular mobility pattern is more realistic. In this paper, we consider abstract attacks on services that provide location information on other users in the proximity. In that setting, we quantify the required effort of the attacker to localize a particular mobile victim. We prove upper and lower bounds for the effort of an optimal attacker. We experimentally show that a Linear Jump Strategy (LJS) practically achieves the upper bounds for almost uniform initial distributions of victims. To improve performance for less uniform distributions known to the attacker, we propose a Greedy Updating Attack Strategy (GUAS). Finally, we derive a realistic mobility model from a real-world dataset and discuss the performance of our strategies in that setting. © 2018, Springer Nature Switzerland AG.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1007/978-3-319-98989-1_19
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/22527
dc.language.isoengspa
dc.publisherSpringer Verlagspa
dc.relation.citationEndPage392
dc.relation.citationStartPage373
dc.relation.citationTitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.citationVolumeVol. 11099 LNCS
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.11099 LNCS,(2018); pp. 373-392spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85051865842&doi=10.1007%2f978-3-319-98989-1_19&partnerID=40&md5=cc65c09ad6b87f30ab1d65089ff24fcdspa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordSecurity of dataspa
dc.subject.keywordSecurity systemsspa
dc.subject.keywordAnalytical boundsspa
dc.subject.keywordAttack strategiesspa
dc.subject.keywordImprove performancespa
dc.subject.keywordLocation informationspa
dc.subject.keywordMobility patternspa
dc.subject.keywordRealistic mobility modelsspa
dc.subject.keywordUniform distributionspa
dc.subject.keywordUpper and lower boundsspa
dc.subject.keywordLocationspa
dc.titleLocation proximity attacks against mobile targets: Analytical bounds and attacker strategiesspa
dc.typeconferenceObjecteng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaDocumento de conferenciaspa
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