Machine learning has started to be deployed in fields such as healthcare and
finance, which propelled the need for and growth of privacy-preserving machine
learning (PPML). We propose an actively secure four-party protocol (4PC), and a
framework for PPML, showcasing its applications on four of the most
widely-known machine learning algorithms -- Linear Regression, Logistic
Regression, Neural Networks, and Convolutional Neural Networks. Our 4PC
protocol tolerating at most one malicious corruption is practically efficient
as compared to the existing works. We use the protocol to build an efficient
mixed-world framework (Trident) to switch between the Arithmetic, Boolean, and
Garbled worlds. Our framework operates in the offline-online paradigm over
rings and is instantiated in an outsourced setting for machine learning. Also,
we propose conversions especially relevant to privacy-preserving machine
learning. The highlights of our framework include using a minimal number of
expensive circuits overall as compared to ABY3. This can be seen in our
technique for truncation, which does not affect the online cost of
multiplication and removes the need for any circuits in the offline phase. Our
B2A conversion has an improvement of $\mathbf{7} \times$ in rounds and
$\mathbf{18} \times$ in the communication complexity. The practicality of our
framework is argued through improvements in the benchmarking of the
aforementioned algorithms when compared with ABY3. All the protocols are
implemented over a 64-bit ring in both LAN and WAN settings. Our improvements
go up to $\mathbf{187} \times$ for the training phase and $\mathbf{158} \times$
for the prediction phase when observed over LAN and WAN.

提出了一种用于隐私保护机器学习的安全四方协议和框架，在广泛使用的四种机器学习算法上进行了应用，并在离线 - online 模式下操作。实验结果表明，在 LAN 和 WAN 环境下，与现有方法相比，该框架在训练和预测阶段的效果提高了 187 倍和 158 倍。