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dc.creatorWang X. 
dc.creatorHou X. 
dc.creatorRios R. 
dc.creatorHallgren P. 
dc.creatorTippenhauer N.O. 
dc.creatorOchoa M.
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.
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.11099 LNCS,(2018); pp. 373-392
dc.titleLocation proximity attacks against mobile targets: Analytical bounds and attacker strategies
dc.publisherSpringer Verlag
dc.subject.keywordSecurity of data
dc.subject.keywordSecurity systems
dc.subject.keywordAnalytical bounds
dc.subject.keywordAttack strategies
dc.subject.keywordImprove performance
dc.subject.keywordLocation information
dc.subject.keywordMobility pattern
dc.subject.keywordRealistic mobility models
dc.subject.keywordUniform distribution
dc.subject.keywordUpper and lower bounds
dc.type.spaDocumento de conferencia
dc.rights.accesoAbierto (Texto Completo)
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.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR

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