PentaGOD: Stepping beyond Traditional GOD with Five Parties

Koti, N and Kukkala, VB and Patra, A and Raj Gopal, B (2022) PentaGOD: Stepping beyond Traditional GOD with Five Parties. In: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (CCS ’22), November 7– 11, 2022, Los Angeles, pp. 1843-1856.

PDF CCS22_2022.pdf - Published Version Restricted to Registered users only Download (1MB) | Request a copy

Abstract

Secure multiparty computation (MPC) is increasingly being used to address privacy issues in various applications. The recent work of Alon et al. (CRYPTO'20) identified the shortcomings of traditional MPC and defined a Friends-and-Foes (FaF) security notion to address the same. We showcase the need for FaF security in real-world applications such as dark pools. This subsequently necessitates designing concretely efficient FaF-secure protocols. Towards this, keeping efficiency at the center stage, we design ring-based FaF-secure MPC protocols in the small-party honest-majority setting. Specifically, we provide (1,1)-FaF secure 5 party computation protocols (5PC) that consider one malicious and one semi-honest corruption and constitutes the optimal setting for attaining honest-majority. At the heart of it lies the multiplication protocol that requires a single round of communication with 8 ring elements (amortized). To facilitate having FaF-secure variants for several applications, we design a variety of building blocks optimized for our FaF setting. The practicality of the designed (1,1)-FaF secure 5PC framework is showcased by benchmarking dark pools. In the process, we also improve the efficiency and security of the dark pool protocols over the existing traditionally secure ones. This improvement is witnessed as a gain of up to 62x in throughput compared to the existing ones. Finally, to demonstrate the versatility of our framework, we also benchmark popular deep neural networks. © 2022 ACM.

Item Type:	Conference Paper
Publication:	Proceedings of the ACM Conference on Computer and Communications Security
Publisher:	Association for Computing Machinery
Additional Information:	The copyright for this article belongs to Association for Computing Machinery
Keywords:	Deep neural networks; Lakes; Privacy-preserving techniques, Computation protocols; Dark pool; Friend-and-foe security; Honest majority; Machine-learning; Multiparty computation; Privacy issue; Privacy preserving; Privacy-preserving machine learning; Secure multi-party computation, Efficiency
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	13 Jan 2023 10:12
Last Modified:	13 Jan 2023 10:12
URI:	https://eprints.iisc.ac.in/id/eprint/79133

Actions (login required)

View Item