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White Paper & Bio
The availability of realistic network data plays a significant role
in fostering collaboration and ensuring technical leadership in network
security research. Unfortunately, a host of technical, legal, policy,
and privacy issues limit the ability of operators to produce datasets
for information security testing. One recent solution to providing
low-risk, high-value data is that of trace anonymization---a process
of sanitizing data before release so that information of concern cannot
be extracted.
Network trace anonymization itself is preceded by decades of established
practice in the medical community to anonymize patient records that
are used in medical research. Network traces embody a similar tension
as that which faces the medical community: They are necessary for
a wide range of research in the trends of and remedies to network
``diseases'' (e.g., malware). They also embody significant information
about the networks and individuals represented in the trace, some
of which could be similarly damaging if revealed (e.g, security posture
and procedures of an organization).
While privacy for both medical records and network traffic records
is a problem of statistical inference control, each domain offers
to an adversary different methods to gain information that might be
used to deanonymize a dataset and requires that different types of
information be preserved in the dataset. Indeed, the current uncertainties
about network trace anonymization, from both technical and policy
perspectives, leave the research community in a vulnerable position.
Even as it marches forward with invaluable data collection efforts,
it does so with little understanding of the privacy violations that
may result and with no tools or techniques for evaluating the privacy
risks of disclosing a dataset. Addressing this challenge is essential,
to ensure the continued, yet responsible, availability of network
trace data to support security research. It is this challenge that
we would like to see the research community address in the near term.
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| Fabian
Monrose
Johns Hopkins
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Biographical Data
Fabian Monrose is an Associate Professor in the Computer Science
department at Johns Hopkins. He holds a joint appointment with the
Hopkins Information Security Institute. Prior to joining Hopkins
he was a member of the Secure Systems Group at Bell Labs, Lucent
Technologies. His research interests include computer and network
security, applied cryptography, data anonymization and privacy preserving
techniques, biometrics, network traffic classification, to name
a few.
See http://cs.jhu.edu/~fabian
for more information.
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