  As privacy-preserving becomes a pivotal aspect of deep learning (DL)
development, multi-party computation (MPC) has gained prominence for its
efficiency and strong security. However, the practice of current MPC frameworks
is limited, especially when dealing with large neural networks, exemplified by
the prolonged execution time of 25.8 seconds for secure inference on
ResNet-152. The primary challenge lies in the reliance of current MPC
approaches on additive secret sharing, which incurs significant communication
overhead with non-linear operations such as comparisons. Furthermore, additive
sharing suffers from poor scalability on party size. In contrast, the evolving
landscape of MPC necessitates accommodating a larger number of compute parties
and ensuring robust performance against malicious activities or computational
failures.
  In light of these challenges, we propose SSNet, which for the first time,
employs Shamir's secret sharing (SSS) as the backbone of MPC-based ML
framework. We meticulously develop all framework primitives and operations for
secure DL models tailored to seamlessly integrate with the SSS scheme. SSNet
demonstrates the ability to scale up party numbers straightforwardly and embeds
strategies to authenticate the computation correctness without incurring
significant performance overhead. Additionally, SSNet introduces masking
strategies designed to reduce communication overhead associated with non-linear
operations. We conduct comprehensive experimental evaluations on commercial
cloud computing infrastructure from Amazon AWS, as well as across diverse
prevalent DNN models and datasets. SSNet demonstrates a substantial performance
boost, achieving speed-ups ranging from 3x to 14x compared to SOTA MPC
frameworks. Moreover, SSNet also represents the first framework that is
evaluated on a five-party computation setup, in the context of secure DL
inference.
