Collaborative Research: CT-ISG: Incentive-Compatible Protocols

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NSF 0524139 / 0524030Collaborative Research: Incentive-Compatible ProtocolsRebecca N. Wright, Stevens Institute and Sheng Zhong, State University of New York at BuffaloThe rapid expansion of the Internet has changed the way we communicate, with ever-increasing aspects of our daily life involving computation and data communication. However, many communications protocol designs assume that all participants will follow the specified protocols, and do not maintain their desirable properties if they don't. One approach to this problem is to design for a world in which some parties may maliciously deviate from their protocols in arbitrary ways. However, the resulting designs are typically expensive or impossible to achieve, and in many cases overestimate the kinds of misbehavior one is likely to encounter. Weaker security goals are more achievable, but often underestimate the willingness of participants to cheat.This project studies an intermediate notion of security, known as incentive compatibility. In many practical settings, it is reasonable to assume that users will tend to act in their own best interests. Therefore, if one can design and use protocols that are incentive-compatible, in that following the protocol produces outcomes for participants at least as good as deviating from the protocol, then one can in many settings avoid misbehavior. Incentive compatibility, which borrows ideas from economics and game theory, is a more finely tunable notion of security than traditional security models, allowing different kinds of participants motivated by different incentives. Such solutions can provide better trust and assurance than protocols secure only against passive adversaries, at a potentially lower cost than protocols secure against an arbitrarily malicious adversary. This allows incentive-compatible protocols to provide the "right" level of security, at the "right" cost.Four areas studied are: incentive-compatible ad hoc networks, incentive-compatible data mining, (both of which can significantly benefit from introducing incentive-compatible solutions), testing tools for incentive compatibility, and foundations of incentive compatibility.