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Abdelwahab Boualouache

Researcher at University of Luxembourg

Publications -  33
Citations -  559

Abdelwahab Boualouache is an academic researcher from University of Luxembourg. The author has contributed to research in topics: Vehicular ad hoc network & Computer science. The author has an hindex of 9, co-authored 22 publications receiving 364 citations. Previous affiliations of Abdelwahab Boualouache include University of Science and Technology Houari Boumediene.

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A Survey on Pseudonym Changing Strategies for Vehicular Ad-Hoc Networks

TL;DR: A comprehensive survey and classification of pseudonym changing strategies for VANETs is presented and some current researches, and open issues are highlighted and compared with respect to some relevant criteria.
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A survey on pseudonym changing strategies for Vehicular Ad-Hoc Networks

TL;DR: In this article, a comprehensive survey and classification of pseudonym changing strategies for VANETs is presented and compared with respect to some relevant criteria, highlighting some current researches and open issues and giving some future directions.
Journal ArticleDOI

VLPZ: The Vehicular Location Privacy Zone☆

TL;DR: An analytical model based on queuing theory is developed to evaluate the location privacy protection achieved at a VLPZ using the size of vehicles’ anonymity set as a metric.
Proceedings ArticleDOI

S2SI: A Practical Pseudonym Changing Strategy for Location Privacy in VANETs

TL;DR: A practical pseudonym changing strategy, called Silence & Swap at Signalized Intersection (S2SI), which consists of two protocols, one for creating of safe silent mix zones and another for exchanging pseudonyms between vehicles under the control of RSUs within these zones is proposed.
Journal ArticleDOI

TAPCS: Traffic-aware pseudonym changing strategy for VANETs

TL;DR: A new pseudonym changing strategy called Traffic-Aware Pseudonym Changing Strategy (TAPCS) is proposed to provide an effective location privacy protection against the different types of pseudonyms linking attacks that can be performed by a strong passive adversary model.