Title:
A-Taxonomy-and-Adversarial-Model-for
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Description: In recent years, it has become important for researchers, security
incident responders and educators to share network
logs, and many log anonymization tools and techniques have
been put forth to sanitize this sensitive data source in order
to enable more collaboration. Unfortunately, many new
attacks have been created, in parallel, that try to exploit
weaknesses in the anonymization process. In this paper, we
present a taxonomy that relates similar kinds of attacks in
a meaningful way. We also present a new adversarial model
which we can map into the taxonomy by the types of attacks
that can be perpetrated by a particular adversary. This has
helped us to negotiate the trade-offs between data utility
and trust, by giving us a way to specify the strength of an
anonymization scheme as a measure of the types of adversaries
it protects against.
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