41,294 research outputs found
Evaluating On-demand Pseudonym Acquisition Policies in Vehicular Communication Systems
Standardization and harmonization efforts have reached a consensus towards
using a special-purpose Vehicular Public-Key Infrastructure (VPKI) in upcoming
Vehicular Communication (VC) systems. However, there are still several
technical challenges with no conclusive answers; one such an important yet open
challenge is the acquisition of shortterm credentials, pseudonym: how should
each vehicle interact with the VPKI, e.g., how frequently and for how long?
Should each vehicle itself determine the pseudonym lifetime? Answering these
questions is far from trivial. Each choice can affect both the user privacy and
the system performance and possibly, as a result, its security. In this paper,
we make a novel systematic effort to address this multifaceted question. We
craft three generally applicable policies and experimentally evaluate the VPKI
system performance, leveraging two large-scale mobility datasets. We consider
the most promising, in terms of efficiency, pseudonym acquisition policies; we
find that within this class of policies, the most promising policy in terms of
privacy protection can be supported with moderate overhead. Moreover, in all
cases, this work is the first to provide tangible evidence that the
state-of-the-art VPKI can serve sizable areas or domain with modest computing
resources.Comment: 6 pages, 7 figures, IoV-VoI'1
Invisible Pixels Are Dead, Long Live Invisible Pixels!
Privacy has deteriorated in the world wide web ever since the 1990s. The
tracking of browsing habits by different third-parties has been at the center
of this deterioration. Web cookies and so-called web beacons have been the
classical ways to implement third-party tracking. Due to the introduction of
more sophisticated technical tracking solutions and other fundamental
transformations, the use of classical image-based web beacons might be expected
to have lost their appeal. According to a sample of over thirty thousand images
collected from popular websites, this paper shows that such an assumption is a
fallacy: classical 1 x 1 images are still commonly used for third-party
tracking in the contemporary world wide web. While it seems that ad-blockers
are unable to fully block these classical image-based tracking beacons, the
paper further demonstrates that even limited information can be used to
accurately classify the third-party 1 x 1 images from other images. An average
classification accuracy of 0.956 is reached in the empirical experiment. With
these results the paper contributes to the ongoing attempts to better
understand the lack of privacy in the world wide web, and the means by which
the situation might be eventually improved.Comment: Forthcoming in the 17th Workshop on Privacy in the Electronic Society
(WPES 2018), Toronto, AC
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