Location proximity attacks against mobile targets: Analytical bounds and attacker strategies
Paper i proceeding, 2018

Location 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.


Xueou Wang

Singapore University of Technology and Design

Xiaolu Hou

Nanyang Technological University

Ruben Rios

Universidad De Malaga

Per Hallgren

Einride AB

Chalmers, Data- och informationsteknik, Informationssäkerhet

Nils Ole Tippenhauer

Singapore University of Technology and Design

Martin Ochoa

Colegio Mayor de Nuestra Senora del Rosario

Singapore University of Technology and Design

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 11099 LNCS 373-392
978-3-319-98988-4 (ISBN)

23rd European Symposium on Research in Computer Security, ESORICS 2018
Barcelona, Spain,


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