16th USENIX Security Symposium – Abstract
Pp. 339–352 of the Proceedings
On Web Browsing Privacy in Anonymized NetFlows
S.E. Coull, Johns Hopkins University; M.P. Collins, Carnegie Mellon University; C.V. Wright and F. Monrose, Johns Hopkins University; M.K. Reiter, Carnegie Mellon University
Abstract
Anonymization of network traces is widely viewed as a necessary
condition for releasing such data for research purposes. For
obvious privacy reasons, an important goal of trace anonymization is
to suppress the recovery of web browsing activities. While several
studies have examined the possibility of reconstructing web
browsing activities from anonymized packet-level traces, we argue
that these approaches fail to account for a number of challenges
inherent in real-world network traffic, and more so, are unlikely to
be successful on coarser NetFlow logs.
By contrast, we develop new approaches that identify
target web pages within anonymized NetFlow data, and address many
real-world challenges, such as browser caching and session
parsing.
We evaluate the effectiveness of our techniques in identifying
front pages from the 50 most popular web sites on the Internet (as ranked by alexa.com), in
both a closed-world experiment similar to that of earlier work and
in tests with real network flow logs.
Our results show that certain types of web pages with unique and
complex structure remain identifiable despite the use of state-of-the-art
anonymization techniques. The concerns raised herein pose a threat to web
browsing privacy insofar as the attacker can approximate the web
browsing conditions represented in the flow logs.
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