As malicious attacks on computer systems increase in severity and
sophistication, developing effective methods for protecting the Internet
is among the most important challenges facing computer science today.
Network-based security mechanisms offer both good coverage and the
possibility of early threat detection, but they often conflict with
the performance requirements of network elements because of the vast
amounts of traffic data that must be analyzed.
This project will apply massive-dataset (MDS) algorithmics to network
security, bringing together two previously unconnected research areas.
The objective is to achieve a qualitative improvement in network security
by developing efficient, yet theoretically rigorous, algorithmic defenses
that can be deployed at scale in modern networks. The project addresses
both fundamental algorithm-design problems and practical applications.
This project is supported by the NSF grants
(Oct 1, 2007 - Sep 30, 2010).
- J. Feigenbaum, A. Johnson, and P. Syverson.
Preventing Active Timing Attacks in Low-Latency Anonymous
10th Privacy Enhancing Technologies Symposium, 2010.
- J. Feigenbaum, A. Jaggard, and M. Schapira.
Approximate Privacy: Foundations and Quantifications.
11th ACM Conference on Electronic Commerce, 2010.
- F. Saint-Jean and J. Feigenbaum.
Usability of Browser-Based Tools for Web-Search Privacy.
Yale University, Department of Computer Science Technical Report 1424.
- A. Johnson.
Design and Analysis of Efficient Anonymous-Communication
PhD Thesis, Computer Science Department, Yale University, 2009.
- A. Johnson and P. Syverson.
More Anonymous Onion Routing through Trust.
22nd IEEE Computer Security Foundations Symposium, 2009.
- J. Feigenbaum, S. Kannan, M. Strauss, and M. Viswanathan.
Testing and Spot-Checking of Data Streams.
Algorithmica 34, 2002.
Contact: shmat AT cs DOT utexas DOT edu