2022
DOI: 10.1007/978-3-031-06944-4_12
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Round-Optimal Multi-party Computation with Identifiable Abort

Abstract: Secure multi-party computation (MPC) protocols that are resilient to a dishonest majority allow the adversary to get the output of the computation while, at the same time, forcing the honest parties to abort. Aumann and Lindell introduced the enhanced notion of security with identifiable abort, which still allows the adversary to trigger an abort but, at the same time, it enables the honest parties to agree on the identity of the party that led to the abort. More recently, in Eurocrypt 2016, Garg et al. showed… Show more

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Cited by 4 publications
(1 citation statement)
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“…Secure multiparty computation (MPC) [Yao86, GMW87] allows a group of mutually distrustful parties to jointly evaluate any function over their private inputs in such a way that no one learns anything beyond the output of the function. Since its introduction, MPC has been extensively studied in terms of assumptions, complexity, security definitions, and execution models [GMW87, Kil88, IPS08, BMR90, KOS03, KO04, Pas04, PW10, Wee10, Goy11, GMPP16, ACJ17, BHP17, COWZ22,CRSW22]. In [GMPP16] Garg et al established that four rounds are necessary to securely realize non-trivial functionalities (e.g., coin tossing) in the plain model with static corruption and black-box simulation 6 in the dishonest majority setting.…”
Section: Table Of Contents 1 Introductionmentioning
confidence: 99%
“…Secure multiparty computation (MPC) [Yao86, GMW87] allows a group of mutually distrustful parties to jointly evaluate any function over their private inputs in such a way that no one learns anything beyond the output of the function. Since its introduction, MPC has been extensively studied in terms of assumptions, complexity, security definitions, and execution models [GMW87, Kil88, IPS08, BMR90, KOS03, KO04, Pas04, PW10, Wee10, Goy11, GMPP16, ACJ17, BHP17, COWZ22,CRSW22]. In [GMPP16] Garg et al established that four rounds are necessary to securely realize non-trivial functionalities (e.g., coin tossing) in the plain model with static corruption and black-box simulation 6 in the dishonest majority setting.…”
Section: Table Of Contents 1 Introductionmentioning
confidence: 99%