bloessl2015scrambler
Bastian Bloessl, Christoph Sommer, Falko Dressler and David Eckhoff, "The Scrambler Attack: A Robust Physical Layer Attack on Location Privacy in Vehicular Networks," Proceedings of IEEE International Conference on Computing, Networking and Communications (ICNC 2015), CNC Workshop, Anaheim, CA, February 2015, pp. 395–400.
Abstract
Vehicular networks provide the basis for a wide range of both safety and non-safety applications. One of the key challenges for wide acceptance is to which degree the drivers’ privacy can be protected. The main technical privacy protection mechanism is the use of changing identifiers (from MAC to application layer), so called pseudonyms. The effectiveness of this approach, however, is clearly reduced if specific characteristics of the physical layer (e.g., in the transmitted signal) reveal the link between two messages with different pseudonyms. In this paper, we present such a fingerprinting technique: the scrambler attack. In contrast to other physical layer fingerprinting methods, it does not rely on potentially fragile features of the channel or the hardware, but exploits the transmitted scrambler state that each receiver has to derive in order to decode a packet, making this attack extremely robust. We show how the scrambler attack bypasses the privacy protection mechanism of state-of-the-art approaches and quantify the degradation of drivers' location privacy with an extensive simulation study. Based on our results, we identify additional technological requirements in order to enable privacy protection mechanisms on a large scale.
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BibTeX reference
@inproceedings{bloessl2015scrambler,
author = {Bloessl, Bastian and Sommer, Christoph and Dressler, Falko and Eckhoff, David},
title = {{The Scrambler Attack: A Robust Physical Layer Attack on Location Privacy in Vehicular Networks}},
booktitle = {IEEE International Conference on Computing, Networking and Communications (ICNC 2015), CNC Workshop},
address = {Anaheim, CA},
doi = {10.1109/ICCNC.2015.7069376},
month = {February},
pages = {395--400},
publisher = {IEEE},
year = {2015},
}
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