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She has a long-standing interest in fundamental problems in complexity theory that are motivated by cryptology and is co-inventor (with former colleagues Matt Blaze and Jack Lacy) of the security-research area of "trust management." More recently, she has worked on basic algorithms for massive data sets, particularly those generated in network operations and business-to-consumer e-commerce. With collaborators Sampath Kannan, Martin Strauss, and Mahesh Viswanathan, Professor Feigenbaum has devised several highly influential algorithms for network-generated massive data, including a randomized algorithm for deciding whether two streams of router measurements are approximately equivalent and another for deciding whether a stream is close to having the "groupedness" property (a natural relaxation of the sortedness property). Within the area of e-commerce foundations, she has also worked on the interplay of incentives and computation. Using tools from microeconomics and game theory, computer scientists are now developing a theory of "incentive-compatible" distributed computation. In joint work with Arvind Krishnamurthy, Christos Papadimitriou, Rahul Sami, and Scott Shenker, Professor Feigenbaum has studied incentive-compatible protocols for multicast cost/sharing and interdomain routing.
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NEW SECURITY PARADIGMS WORKSHOP, NSPW 2022pp.117-129, (2023)
arxiv(2022)
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Democratizing Cryptographypp.157-184, (2022)
Proceedings of the 19th Workshop on Privacy in the Electronic Society (2020)
Found. Trends Priv. Secur.no. 4 (2020): 247-399
CSLAW '19: Proceedings of the Symposium on Computer Science and Law (2020)
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user-60d14cd84c775e0497060202(2020)
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Proceedings on Privacy Enhancing Technologiesno. 4 (2020): 24-47
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