Complex machine learning (ML) inference algorithms like recurrent neural networks (RNNs) use standard functions from math libraries like exponentiation, sigmoid, tanh, and reciprocal of square root. Although prior work on secure 2-party inference provides specialized protocols for convolutional neural networks (CNNs), existing secure implementations of these math operators rely on generic 2-party computation (2PC) protocols that suffer from high communication. We provide new specialized 2PC protocols for math functions that crucially rely on lookup-tables and mixed-bitwidths to address this performance overhead; our protocols for math functions communicate up to 423x less data than prior work. Some of the mixed bitwidth operations used by our math implementations are (zero and signed) extensions, different forms of truncations, multiplication of operands of mixed-bitwidths, and digit decomposition (a generalization of bit decomposition to larger digits). For each of these primitive operations, we construct specialized 2PC protocols that are more communication efficient than generic 2PC, and can be of independent interest. Furthermore, our math implementations are numerically precise, which ensures that the secure implementations preserve model accuracy of cleartext. We build on top of our novel protocols to build SIRNN, a library for end-to-end secure 2-party DNN inference, that provides the first secure implementations of an RNN operating on time series sensor data, an RNN operating on speech data, and a state-of-the-art ML architecture that combines CNNs and RNNs for identifying all heads present in images. Our evaluation shows that SIRNN achieves up to three orders of magnitude of performance improvement when compared to inference of these models using an existing state-of-the-art 2PC framework.

中文翻译：

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SIRNN：用于安全RNN推理的数学库

复杂的机器学习（ML）推理算法（例如递归神经网络（RNN））使用数学库中的标准函数，例如幂运算，S形，正切和平方根倒数。尽管先前关于安全两方推理的工作为卷积神经网络（CNN）提供了专用协议，但是这些数学运算符的现有安全实现方式依赖于通信频繁的通用两方计算（2PC）协议。我们为数学函数提供了新的专用2PC协议，这些协议非常依赖查找表和混合位宽来解决此性能开销；我们的数学函数协议所传递的数据比以前的工作少多达423倍。我们的数学实现使用的一些混合位宽运算是（零和有符号）扩展名，不同形式的截断，混合使用混合位宽的操作数，并进行数字分解（将位分解概括为较大的数字）。对于这些原始操作中的每一个，我们构建专用的2PC协议，这些协议比通用2PC的通信效率更高，并且可能具有独立的意义。此外，我们的数学实现在数值上是精确的，从而确保安全的实现保持明文的模型准确性。我们以新颖的协议为基础，构建了SIRNN，这是一个用于端到端安全的两方DNN推理的库，它提供了对时间序列传感器数据进行操作的RNN，对语音数据进行操作的RNN的第一个安全实现，最新的ML架构结合了CNN和RNN来识别图像中出现的所有头部。