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.

ENSEI是一个基于频域安全卷积协议的安全推理框架，通过同态加密和秘密共享，实现了可忽略执行的同态卷积计算，显著简化了相关的计算，实验结果表明在CIFAR-10数据集上相比最佳已知方案，我们在保证1%的准确率下，将在线时间减少了5--11倍，安装时间减少了高达33倍，总推理时间缩短了10倍，并可在二元神经网络上减少33%的带宽。

ENSEI: 频域同态卷积确保隐私的低开销安全推理技术，用于视觉识别