MOTION2NX -- A Framework for Generic Hybrid Two-Party Computation and Private Inference with Neural Networks
This software is an extension of the MOTION framework for multi-party computation. We additionally implemented five 2PC protocols with passive security together with all 20 possible conversions among each other to enable private evaluation of hybrid circuits:
- Yao's Garbled Circuits with FreeXOR and Half-Gates
- Arithmetic and Boolean variants of Goldreich-Micali-Wigderson
- Arithmetic and Boolean variants of the secret-sharing-based protocols from ABY2.0 (Patra et al., USENIX Security '21)
Moreover, we support private inference with neural networks by providing secure tensor data types and specialized building blocks for common tensor operations. With support of the Open Neural Network Exchange (ONNX) file format, this makes our framework interoperable with industry-standard deep learning frameworks such as TensorFlow and PyTorch.
Compared to the original MOTION codebase, we made architectural improvements to increase flexibility and performance of the framework. Although the interfaces of this work are currently not compatible with the original framework due to the concurrent development of both branches, it is planned to integrate the MOTION2NX features into MOTION itself.
More information about this work is given in this extended abstract which was accepted at the [email protected] 2021 workshop. It is the result of Lennart Braun's master's thesis in the ENCRYPTO group at TU Darmstadt supervised by Thomas Schneider and Rosario Cammarota.
This code is provided as a experimental implementation for testing purposes and should not be used in a productive environment. We cannot guarantee security and correctness.