论文标题
ENSEI：通过频域同型卷积有效的安全推断，以提供隐私视觉识别
ENSEI: Efficient Secure Inference via Frequency-Domain Homomorphic Convolution for Privacy-Preserving Visual Recognition
论文作者
论文摘要
在这项工作中，我们提出了基于频域安全卷积（FDSC）协议的安全推理（SI）框架ENSEI，以有效执行隐私保护视觉识别。我们的观察结果是，在同态加密和秘密共享的结合下，可以在频域中遗忘同型卷积，从而大大简化了相关的计算。我们为基于数字理论变换（NTT）的FDSC提供协议设计和参数推导。在实验中，我们彻底研究了时间和频域同型卷积之间的准确性效率折衷。与Ensei相比，与最知名的作品相比，我们实现了5--11倍的在线时间减少，最高33倍的设置时间缩短，并减少总体推理时间最高10倍。在CIFAR-10数据集上，二进制神经网络中只能获得另外33％的带宽减少。
In this work, we propose ENSEI, a secure inference (SI) framework based on the frequency-domain secure convolution (FDSC) protocol for the efficient execution of privacy-preserving visual recognition. Our observation is that, under the combination of homomorphic encryption and secret sharing, homomorphic convolution can be obliviously carried out in the frequency domain, significantly simplifying the related computations. We provide protocol designs and parameter derivations for number-theoretic transform (NTT) based FDSC. In the experiment, we thoroughly study the accuracy-efficiency trade-offs between time- and frequency-domain homomorphic convolution. With ENSEI, compared to the best known works, we achieve 5--11x online time reduction, up to 33x setup time reduction, and up to 10x reduction in the overall inference time. A further 33% of bandwidth reductions can be obtained on binary neural networks with only 1% of accuracy degradation on the CIFAR-10 dataset.
