Abstract
Recent years have seen a tremendous growth in the interest in secure multiparty computation (MPC) and its applications. While much progress has been made concerning its efficiency, many current, state-of-the-art protocols are vulnerable to Denial of Service attacks, where a cheating party may prevent the honest parties from learning the output of the computation, whilst remaining anonymous. The security model of identifiable abort aims to prevent these attacks, by allowing honest parties to agree upon the identity of a cheating party, who can then be excluded in the future. Several existing MPC protocols offer security with identifiable abort against a dishonest majority of corrupted parties. However, all of these protocols have a round complexity that scales linearly with the depth of the circuit (and are therefore unsuitable for use in high latency networks) or use cryptographic primitives or techniques that have a high computational overhead. In this work, we present the first efficient MPC protocols with identifiable abort in the dishonest majority setting, which run in a constant number of rounds and make only black-box use of cryptographic primitives. Our main construction is built from highly efficient primitives in a careful way to achieve identifiability at a low cost. In particular, we avoid the use of public-key operations outside of a setup phase, incurring a relatively low overhead on top of the fastest currently known constant-round MPC protocols based on garbled circuits. Our construction also avoids the use of adaptively secure primitives and heavy zero-knowledge machinery, which was inherent in previous works. In addition, we show how to upgrade our protocol to achieve public verifiability using a public bulletin board, allowing any external party to verify correctness of the computation or identify a cheating party.
| Original language | English |
|---|---|
| Title of host publication | Advances in Cryptology - CRYPTO 2020 - 40th Annual International Cryptology Conference, CRYPTO 2020, Proceedings |
| Editors | Daniele Micciancio, Thomas Ristenpart |
| Publisher | Springer |
| Pages | 562-592 |
| Number of pages | 31 |
| ISBN (Print) | 9783030568795 |
| DOIs | |
| State | Published - 2020 |
| Externally published | Yes |
| Event | 40th Annual International Cryptology Conference, CRYPTO 2020 - Santa Barbara, United States Duration: 17 Aug 2020 → 21 Aug 2020 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 12171 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 40th Annual International Cryptology Conference, CRYPTO 2020 |
|---|---|
| Country/Territory | United States |
| City | Santa Barbara |
| Period | 17/08/20 → 21/08/20 |
Bibliographical note
Publisher Copyright:© International Association for Cryptologic Research 2020.
Funding
C. Baum—Supported by the European Research Council (ERC) under the European Unions’ Horizon 2020 research and innovation programme under grant agreement No 669255 (MPCPRO) as well as the BIU Center for Research in Applied Cryptography and Cyber Security in conjunction with the Israel National Cyber Bureau in the Prime Minister’s Office. Part of this work was done while the author was at Bar Ilan University. E. Orsini—Supported in part by ERC Advanced Grant ERC-2015-AdG-IMPaCT. P. Scholl—Supported in part by the Danish Independent Research Council under Grant-ID DFF-6108-00169 (FoCC) and an Aarhus University Research Foundation (AUFF) starting grant. E. Soria-Vazquez—Supported by the Carlsberg Foundation under the Semper Ardens Research Project CF18-112 (BCM).
| Funders | Funder number |
|---|---|
| AUFF | |
| European Unions’ Horizon 2020 research and innovation programme | |
| FoCC | |
| Horizon 2020 Framework Programme | 669255, 690978 |
| European Commission | |
| Aarhus Universitets Forskningsfond | |
| Bar-Ilan University | ERC-2015-AdG-IMPaCT |
| Carlsbergfondet | CF18-112 |
| Danmarks Frie Forskningsfond | DFF-6108-00169 |