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MP-SPDZ implements more than 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.

A particular focus of MP-SPDZ is the application to machine learning. Currently this includes inference/classification for complex networks such as ResNet and DenseNet as well as deep learning training with fully-connected layers and softmax/sigmoid output activation.

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