Multiparty computation approaches to secure neural network inference
traditionally rely on garbled circuits for securely executing nonlinear
activation functions. However, garbled circuits require excessive communication
between server and client, impose significant storage overheads, and incur
large runtime penalties. To eliminate these costs, we propose an alternative to
garbled circuits: Tabula, an algorithm based on secure lookup tables. Tabula
leverages neural networks' ability to be quantized and employs a secure lookup
table approach to efficiently, securely, and accurately compute neural network
nonlinear activation functions. Compared to garbled circuits with quantized
inputs, when computing individual nonlinear functions, our experiments show
Tabula uses between $35 \times$-$70 \times$ less communication, is over
$100\times$ faster, and uses a comparable amount of storage. This leads to
significant performance gains over garbled circuits with quantized inputs
during secure inference on neural networks: Tabula reduces overall
communication by up to $9 \times$ and achieves a speedup of up to $50 \times$,
while imposing comparable storage costs.

该研究通过采用安全查找表的算法 Tabula，代替使用加密电路进行神经网络推断所需的非线性激活函数加密计算，以此降低了通信、存储和运行成本，并在安全神经网络推断时实现了显著的性能提升。