  Vertical Federated Learning (VFL) has emerged as one of the most predominant
approaches for secure collaborative machine learning where the training data is
partitioned by features among multiple parties. Most VFL algorithms primarily
rely on two fundamental privacy-preserving techniques: Homomorphic Encryption
(HE) and secure Multi-Party Computation (MPC). Though generally considered with
stronger privacy guarantees, existing general-purpose MPC frameworks suffer
from expensive computation and communication overhead and are inefficient
especially under VFL settings. This study centers around MPC-based VFL
algorithms and presents a novel approach for two-party vertical federated
linear models via an efficient secret sharing (SS) scheme with a trusted
coordinator. Our approach can achieve significant acceleration of the training
procedure in vertical federated linear models of between 2.5x and 6.6x than
other existing MPC frameworks under the same security setting.

Cryptography and Security