A Cryptographic Study of Secure Internet Measurement

Speaker: David Xiao
, Princeton University
Date: April 13 2007
Time: 10:30AM to 12:00PM
Location: 32-G449 Patil/Kiva, Stata Ctr
Contact: Be Blackburn, 3-6098, imbe@mit.edu
The Internet is an indispensable part of our information society, and
yet its basic foundations remain vulnerable to simple attacks, and one
area that remains especially susceptible to attack is routing. There
have been increasing efforts in the networking community to
incorporate security into current routing protocols, and secure
Internet measurement is necessary to inform any routing protocol. In
this talk we will show how to use theoretical tools to give a rigorous
treatment of this security problem. We believe our work shows that
rigorous techniques, and even tools for negative results such as
reducing to one-way functions or black-box separations, can have an
immediate impact on the study of security problems of real-world
importance.
We describe two definitions for this problem: fault detection (FD)
where the honest parties only want to know if the packets they sent
were dropped or modified or not, and fault localization (FL) where the
honest parties want to know in addition where exactly their packets
were modified or dropped. Besides traditional per-packet definitions
where we want to know the fate of every packet, we also propose
*statistical* definitions that reduce the communication and storage
overhead of protocols yet retain useful security properties. We will
sketch constructions of schemes that satisfy our security definitions
and have desirable practical properties.
Next, we show the negative results implied by our definitions. In
particular, we can show the necessity of keys, cryptography, and
storage in any secure FD or FL scheme. We will describe in detail the
proof of our result that any secure black-box construction of a FL
protocol requires cryptography to be performed at each nodes. This
result uses a novel application of the black-box separation technique
of Impagliazzo-Rudich and the learning algorithm of Naor-Rothblum.
This is joint work with Sharon Goldberg, Boaz Barak, and Jennifer Rexford.
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