Efficient Constant-Round Multi-party Computation Combining BMR and SPDZ

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Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, methodology): “Several generic techniques have been developed for MPC including Yao’s garbled circuits protocol [70], the GMW protocol [31], and other secret-sharing based techniques [7, 41, 50].”
Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017, unknown): “generic secure MPC [23], [24], [25], [26], [27].”
Distributed Measurement with Private Set-Union Cardinality (2017, background): “General secure multiparty computation (MPC) protocols can realize any functionality including set-union cardinality, even in the UC model [1, 7], and recent advances in efficient MPC have been made in the multi-party, dishonest-majority setting that we require [10, 33, 34].”
MP-SPDZ: A Versatile Framework for Multi-Party Computation (2020, methodology): “[59] by using SPDZ as the underlying protocol.”
Global-Scale Secure Multiparty Computation (2017, methodology): “However, state-of-the-art protocols based on BMR that are maliciously secure against corruption of n − 1 parties require either O (n) somewhat homomorphic encryptions [32] or O (n) SPDZ multiplication subprotocols [31] per AND gate both of which are relatively inefficient.”