Scalable Multiparty Garbling
Multiparty garbling is the most popular approach for constant-round secure multiparty computation (MPC). Despite being the focus of significant research effort, instantiating prior approaches to multiparty garbling results in constant-round MPC that can not realistically accommodate large numbers of...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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ACM|Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security
2023
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| Online Access: | https://hdl.handle.net/1721.1/153138 |
| _version_ | 1826197060559306752 |
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| author | Beck, Gabrielle Goel, Aarushi Hegde, Aditya Jain, Abhishek Jin, Zhengzhong Kaptchuk, Gabriel |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Beck, Gabrielle Goel, Aarushi Hegde, Aditya Jain, Abhishek Jin, Zhengzhong Kaptchuk, Gabriel |
| author_sort | Beck, Gabrielle |
| collection | MIT |
| description | Multiparty garbling is the most popular approach for constant-round secure multiparty computation (MPC). Despite being the focus of significant research effort, instantiating prior approaches to multiparty garbling results in constant-round MPC that can not realistically accommodate large numbers of parties. In this work we present the first global-scale multiparty garbling protocol. The per-party communication complexity of our protocol decreases as the number of parties participating in the protocol increases - for the first time matching the asymptotic communication complexity of non-constant round MPC protocols. Our protocol achieves malicious security in the honest-majority setting and relies on the hardness of the Learning Party with Noise assumption. |
| first_indexed | 2024-09-23T10:41:53Z |
| format | Article |
| id | mit-1721.1/153138 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:41:53Z |
| publishDate | 2023 |
| publisher | ACM|Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security |
| record_format | dspace |
| spelling | mit-1721.1/1531382024-01-23T18:13:35Z Scalable Multiparty Garbling Beck, Gabrielle Goel, Aarushi Hegde, Aditya Jain, Abhishek Jin, Zhengzhong Kaptchuk, Gabriel Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Multiparty garbling is the most popular approach for constant-round secure multiparty computation (MPC). Despite being the focus of significant research effort, instantiating prior approaches to multiparty garbling results in constant-round MPC that can not realistically accommodate large numbers of parties. In this work we present the first global-scale multiparty garbling protocol. The per-party communication complexity of our protocol decreases as the number of parties participating in the protocol increases - for the first time matching the asymptotic communication complexity of non-constant round MPC protocols. Our protocol achieves malicious security in the honest-majority setting and relies on the hardness of the Learning Party with Noise assumption. 2023-12-12T14:00:25Z 2023-12-12T14:00:25Z 2023-11-15 2023-12-01T08:45:39Z Article http://purl.org/eprint/type/ConferencePaper 979-8-4007-0050-7 https://hdl.handle.net/1721.1/153138 Beck, Gabrielle, Goel, Aarushi, Hegde, Aditya, Jain, Abhishek, Jin, Zhengzhong et al. 2023. "Scalable Multiparty Garbling." PUBLISHER_CC en https://doi.org/10.1145/3576915.3623132 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The author(s) application/pdf ACM|Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security Association for Computing Machinery |
| spellingShingle | Beck, Gabrielle Goel, Aarushi Hegde, Aditya Jain, Abhishek Jin, Zhengzhong Kaptchuk, Gabriel Scalable Multiparty Garbling |
| title | Scalable Multiparty Garbling |
| title_full | Scalable Multiparty Garbling |
| title_fullStr | Scalable Multiparty Garbling |
| title_full_unstemmed | Scalable Multiparty Garbling |
| title_short | Scalable Multiparty Garbling |
| title_sort | scalable multiparty garbling |
| url | https://hdl.handle.net/1721.1/153138 |
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