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Machine learning has started to be deployed in fields such as healthcare andfinance, which propelled the need for and growth of privacy-preserving machinelearning (PPML). We propose an actively secure four-party protocol (4PC), and aframework for PPML, showcasing its applications on four of the mostwidely-known machine learning algorithms -- Linear Regression, LogisticRegression, Neural Networks, and Convolutional Neural Networks. Our 4PCprotocol tolerating at most one malicious corruption is practically efficientas compared to the existing works. We use the protocol to build an efficientmixed-world framework (Trident) to switch between the Arithmetic, Boolean, andGarbled worlds. Our framework operates in the offline-online paradigm overrings and is instantiated in an outsourced setting for machine learning. Also,we propose conversions especially relevant to privacy-preserving machinelearning. The highlights of our framework include using a minimal number ofexpensive circuits overall as compared to ABY3. This can be seen in ourtechnique for truncation, which does not affect the online cost ofmultiplication and removes the need for any circuits in the offline phase. OurB2A conversion has an improvement of $\mathbf{7} \times$ in rounds and$\mathbf{18} \times$ in the communication complexity. The practicality of ourframework is argued through improvements in the benchmarking of theaforementioned algorithms when compared with ABY3. All the protocols areimplemented over a 64-bit ring in both LAN and WAN settings. Our improvementsgo up to $\mathbf{187} \times$ for the training phase and $\mathbf{158} \times$for the prediction phase when observed over LAN and WAN.

Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning