MP-SPDZ: A Versatile Framework for Multi-Party Computation

By Marcel Keller. 2020

659 citations recorded by the source corpus.

Multi-Protocol SPDZ (MP-SPDZ) is a fork of SPDZ-2 (Keller et al., CCS '13), an implementation of the multi-party computation (MPC) protocol called SPDZ (Damgård et al., Crypto '12). MP-SPDZ extends SPDZ-2 to 30 MPC protocol variants, all of which can be used with the same high-level programming interface based on Python. This considerably simplifies comparing the cost of different protocols and security models. The protocols cover all commonly used security models (honest/dishonest majority and semi-honest/malicious corruption) as well as computation of binary and arithmetic circuits (the latter modulo primes and powers of two). The underlying primitives employed include secret sharing, oblivious transfer, homomorphic encryption, and garbled circuits. The breadth of implemented protocols coupled with an accessible high-level interface makes it suitable to benchmark the cost of computation in various security models for researchers both with and without a background in secure computation This paper aims to outline the variety of protocols implemented and the design choices made in the development of MP-SPDZ as well as the capabilities of the programming interface.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Marcel Keller
2. Daniel E. Escudero
3. Assi Barak
4. Anders Dalskov
5. Yehuda Lindell
6. Peter Scholl

Citations in the corpus

• Secure Evaluation of Quantized Neural Networks (2019, methodology): “That is, we investigate how applicable general purpose MPC frameworks such as MPSPDZ [41] or SCALE-MAMBA [1] are to evaluate Convolutional Neural Networks.”