Optimized batched homomorphic encryption with a new pipeline architecture delivers 1.78x faster amortized inference and 3.74x lower memory than prior work for ResNet-20 on 512 encrypted CIFAR-10 images.
On lattices, learning with errors, random linear codes, and cryptography.Procedings of the thirty-seventh annual ACM symposium on Theory of Computing
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Towards Deep Encrypted Training: Low-Latency, Memory-Efficient, and High-Throughput Inference for Privacy-Preserving Neural Networks
Optimized batched homomorphic encryption with a new pipeline architecture delivers 1.78x faster amortized inference and 3.74x lower memory than prior work for ResNet-20 on 512 encrypted CIFAR-10 images.