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MOTION - A Framework for Mixed-Protocol Multi-Party Computation

Braun, Lennart ; Demmler, Daniel ; Schneider, Thomas ; Tkachenko, Oleksandr (2022):
MOTION - A Framework for Mixed-Protocol Multi-Party Computation.
In: ACM Transactions on Privacy and Security, 25 (2), ACM, ISSN 2471-2566,
DOI: 10.1145/3490390,
[Article]

Abstract

We present MOTION, an efficient and generic open-source framework for mixed-protocol secure multi-party computation (MPC). MOTION is built in a user-friendly, modular, and extensible way, intended to be used as a tool in MPC research and to increase adoption of MPC protocols in practice. Our framework incorporates several important engineering decisions such as full communication serialization, which enables MPC over arbitrary messaging interfaces and removes the need of owning network sockets. MOTION also incorporates several performance optimizations that improve the communication complexity and latency, e.g., 2×

better online round complexity of precomputed correlated Oblivious Transfer (OT).

We instantiate our framework with protocols for N parties and security against up to N−1

passive corruptions: the MPC protocols of Goldreich-Micali-Wigderson (GMW) in its arithmetic and Boolean version and OT-based BMR (Ben-Efraim et al., CCS’16), as well as novel and highly efficient conversions between them, including a non-interactive conversion from BMR to arithmetic GMW.

MOTION is highly efficient, which we demonstrate in our experiments. Compared to secure evaluation of AES-128 with N=3 parties in a high-latency network with OT-based BMR, we achieve a 16× better throughput of 16 AES evaluations per second using BMR. With this, we show that BMR is much more competitive than previously assumed. For N=3 parties and full-threshold protocols in a LAN, MOTION is 10×–18× faster than the previous best passively secure implementation from the MP-SPDZ framework, and 190×–586× faster than the actively secure SCALE-MAMBA framework. Finally, we show that our framework is highly efficient for privacy-preserving neural network inference.

Item Type: Article
Erschienen: 2022
Creators: Braun, Lennart ; Demmler, Daniel ; Schneider, Thomas ; Tkachenko, Oleksandr
Title: MOTION - A Framework for Mixed-Protocol Multi-Party Computation
Language: English
Abstract:

We present MOTION, an efficient and generic open-source framework for mixed-protocol secure multi-party computation (MPC). MOTION is built in a user-friendly, modular, and extensible way, intended to be used as a tool in MPC research and to increase adoption of MPC protocols in practice. Our framework incorporates several important engineering decisions such as full communication serialization, which enables MPC over arbitrary messaging interfaces and removes the need of owning network sockets. MOTION also incorporates several performance optimizations that improve the communication complexity and latency, e.g., 2×

better online round complexity of precomputed correlated Oblivious Transfer (OT).

We instantiate our framework with protocols for N parties and security against up to N−1

passive corruptions: the MPC protocols of Goldreich-Micali-Wigderson (GMW) in its arithmetic and Boolean version and OT-based BMR (Ben-Efraim et al., CCS’16), as well as novel and highly efficient conversions between them, including a non-interactive conversion from BMR to arithmetic GMW.

MOTION is highly efficient, which we demonstrate in our experiments. Compared to secure evaluation of AES-128 with N=3 parties in a high-latency network with OT-based BMR, we achieve a 16× better throughput of 16 AES evaluations per second using BMR. With this, we show that BMR is much more competitive than previously assumed. For N=3 parties and full-threshold protocols in a LAN, MOTION is 10×–18× faster than the previous best passively secure implementation from the MP-SPDZ framework, and 190×–586× faster than the actively secure SCALE-MAMBA framework. Finally, we show that our framework is highly efficient for privacy-preserving neural network inference.

Journal or Publication Title: ACM Transactions on Privacy and Security
Volume of the journal: 25
Issue Number: 2
Publisher: ACM
Uncontrolled Keywords: Engineering, E4
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Cryptography and Privacy Engineering (ENCRYPTO)
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1119: CROSSING – Cryptography-Based Security Solutions: Enabling Trust in New and Next Generation Computing Environments
Date Deposited: 22 Jun 2022 12:56
DOI: 10.1145/3490390
URL / URN: https://dl.acm.org/journal/tops
Additional Information:

Art.No.: 8

PPN:
Alternative keywords:
Alternative keywordsLanguage
Secure multi-party computation, outsourcing, hybrid protocols, efficiencyUNSPECIFIED
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