David I. August
Professor in the Department of Computer Science, Princeton University
Affiliated with the Department of Electrical Engineering, Princeton University
Ph.D. May 2000, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

Office: Computer Science Building Room 221
Email: august@princeton.edu
PGP: Public Key
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Fax: (609) 964-1699
Administrative Assistant: Pamela DelOrefice, (609) 258-5551

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A General Approach for Efficiently Accelerating Software-based Dynamic Data Flow Tracking on Commodity Hardware [abstract] (PDF)
Kangkook Jee, Georgios Portokalidis, Vasileios P. Kemerlis, Soumyadeep Ghosh, David I. August, and Angelos D. Keromytis
Proceedings of the 19th Internet Society (ISOC) Symposium on Network and Distributed Systems Security (NDSS), February 2012.
Accept Rate: 17% (46/258).

Despite the demonstrated usefulness of dynamic data flow tracking (DDFT) techniques in a variety of security applications, the poor performance achieved by available prototypes prevents their widespread adoption and use in production systems. We present and evaluate a novel methodology for improving the performance overhead of DDFT frameworks, by combining static and dynamic analysis. Our intuition is to separate the program logic from the corresponding tracking logic, extracting the semantics of the latter and abstracting them using a Taint Flow Algebra. We then apply optimization techniques to eliminate redundant tracking logic and minimize interference with the target program. Our optimizations are directly applicable to binary-only software and do not require any high level semantics. Furthermore, they do not require additional resources to improve performance, neither do they restrict or remove functionality. Most importantly, our approach is orthogonal to optimizations devised in the past, and can deliver additive performance benefits. We extensively evaluate the correctness and impact of our optimizations by augmenting a freely available high-performance DDFT framework, and applying it to multiple applications, including command line utilities, server applications, language runtimes, and web browsers. Our results show a speedup of DDFT by as much as 2.23x, with an average of 1.72x across all tested applications.