The superior performance of deep learning has propelled the rise of Deep Learning as a Service, enabling users to transmit their private data to service providers for model execution and inference retrieval. Nevertheless, the primary concern remains safeguarding the confidentiality of sensitive user data while optimizing the efficiency of secure protocols. To address this, we develop a fast oblivious binarized neural network inference framework, FOBNN. Specifically, we customize binarized convolutional neural networks to enhance oblivious inference, design two fast algorithms for binarized convolutions, and optimize network structures experimentally under constrained costs. Initially, we meticulously analyze the range of intermediate values in binarized convolutions to minimize bit representation, resulting in the Bit Length Bounding (BLB) algorithm. Subsequently, leveraging the efficiency of bitwise operations in BLB, we further enhance performance by employing pure bitwise operations for each binary digit position, yielding the Layer-wise Bit Accumulation (LBA) algorithm. Theoretical analysis validates FOBNN's security and indicates up to $2 \times$ improvement in computational and communication costs compared to the state-of-the-art method. We demonstrates our framework's effectiveness in RNA function prediction within bioinformatics. Rigorous experimental assessments confirm that our oblivious inference solutions not only maintain but often exceed the original accuracy, surpassing prior efforts.

翻译：深度学习的卓越性能推动了深度学习即服务的兴起，使用户能够将私有数据传输给服务提供商进行模型执行和推理结果获取。然而，在优化安全协议效率的同时，保障敏感用户数据的机密性仍是核心挑战。为此，我们提出了一种快速隐匿二值化神经网络推理框架FOBNN。具体而言，我们定制了二值化卷积神经网络以增强隐匿推理，设计了两种二值化卷积快速算法，并在受限成本下通过实验优化网络结构。首先，我们精细分析二值化卷积中间值的取值范围以最小化比特表示，提出了比特长度边界（BLB）算法。随后，利用BLB中按位运算的高效性，我们进一步通过在每个二进制数位上采用纯按位运算提升性能，提出了逐层比特累加（LBA）算法。理论分析验证了FOBNN的安全性，并表明与当前最优方法相比，其计算与通信成本可提升高达2倍。我们展示了该框架在生物信息学RNA功能预测中的有效性。严格的实验评估证实，我们的隐匿推理方案不仅保持了原始精度，且常能超越先前成果。