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Main navigation | Main content
Monday, February 11, 2013
| Presenter: | Stephen McCamant |
|---|---|
| Affiliation: | University of Minnesota |
| Website: | http://www.cs.umn.edu/people/faculty/mccamant |
| Time: | 11:15 - 12:15 |
| Location: | Keller Hall 3-125 |
| Host: | Nick Hopper |
Current computing systems provide little support for determining when a computation reveals information that it should not. Information leakage is a common kind of security failure, but it can be difficult to detect while it is happening. Quantitative information-flow techniques let us measure the number of bits of information about secret computation inputs that are revealed by computation outputs. For instance, does an image de-identification transformation retain too much information? Does a network game leak strategic details? In this talk I'll give an overview of a family of techniques for information-flow measurement, using both static and dynamic program analysis. By using scalable algorithms and instruction-level analysis, we applied these techniques to a number of existing large applications, and in the process discovered a previously-unknown bug.
Bio: Stephen McCamant is an assistant professor in the Department of Computer Science and Engineering at the University of Minnesota. His primary research interest is applications of program analysis techniques for software security and correctness. This includes binary analysis and transformation, hybrids of dynamic and static analysis including symbolic execution, information flow and taint analysis, instruction-level hardening and isolation, and applications of decision procedures and proof-assistant tools. Before joining Minnesota, he spent 2008-2012 as a postdoc at the University of California, Berkeley, and received his Ph.D. in 2008 from MIT.
