TY - GEN
T1 - Rationality and adversarial behavior in multi-party computation
AU - Lysyanskaya, Anna
AU - Triandopoulos, Nikos
PY - 2006
Y1 - 2006
N2 - We study multi-party computation in the model where none of n participating parties are honest: they are either rational, acting in their selfish interest to maximize their utility, or adversarial, acting arbitrarily. In this new model, which we call the mixed-behavior model, we define a class of functions that can be computed in the presence of an adversary using a trusted mediator. We then give a protocol that allows the rational parties to emulate the mediator and jointly compute the function such that (1) assuming that each rational party prefers that it learns the output while others do not, no rational party has an incentive to deviate from the protocol; and (2) the rational parties are protected from a malicious adversary controlling ⌈n/2⌉ - 2 of the participants: the adversary can only either cause all rational participants to abort (so no one learns the function they are trying to compute), or can only learn whatever information is implied by the output of the function.
AB - We study multi-party computation in the model where none of n participating parties are honest: they are either rational, acting in their selfish interest to maximize their utility, or adversarial, acting arbitrarily. In this new model, which we call the mixed-behavior model, we define a class of functions that can be computed in the presence of an adversary using a trusted mediator. We then give a protocol that allows the rational parties to emulate the mediator and jointly compute the function such that (1) assuming that each rational party prefers that it learns the output while others do not, no rational party has an incentive to deviate from the protocol; and (2) the rational parties are protected from a malicious adversary controlling ⌈n/2⌉ - 2 of the participants: the adversary can only either cause all rational participants to abort (so no one learns the function they are trying to compute), or can only learn whatever information is implied by the output of the function.
UR - http://www.scopus.com/inward/record.url?scp=33749541500&partnerID=8YFLogxK
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U2 - 10.1007/11818175_11
DO - 10.1007/11818175_11
M3 - Conference contribution
AN - SCOPUS:33749541500
SN - 3540374329
SN - 9783540374329
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 180
EP - 197
BT - Advances in Cryptology - CRYPTO 2006 - 26th Annual International Cryptology Conference, Proceedings
T2 - 26th Annual International Cryptology Conference, CRYPTO 2006
Y2 - 20 August 2006 through 24 August 2006
ER -