MPCLeague: Robust 4-party Computation for Privacy-Preserving Machine Learning

Secure computation has demonstrated its potential in several practical use-cases, particularly in privacy-preserving machine learning (PPML). Robustness, the property that guarantees output delivery irrespective of adversarial behaviour, and efficiency, are the two first-order asks of a successfully deployable PPML framework. Towards this, we propose the first robust, highly-efficient mixed-protocol framework, MPCLeague that works with four parties, offers malicious security, and supports ring. MPCLeague has a multifold improvement over ABY3 (Mohassel et al. CCS'18), a 3-party framework achieving security with abort, and improves upon Trident (Chaudhari et al. NDSS’20), a 4-party framework achieving security with fairness. MPCLeague's competence is tested with extensive benchmarking for deep neural networks such as LeNet and VGG16, and support vector machines.