System-level evaluation reveals that network constraints and hardware costs, rather than raw latency, often dictate the optimal choice between MPC and FHE for privacy-preserving ML.
Toward practical privacy-preserving convolutional neural networks exploiting fully homomorphic encryption
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Incorporating fully homomorphic encryption (FHE) into the inference process of a convolutional neural network (CNN) draws enormous attention as a viable approach for achieving private inference (PI). FHE allows delegating the entire computation process to the server while ensuring the confidentiality of sensitive client-side data. However, practical FHE implementation of a CNN faces significant hurdles, primarily due to FHE's substantial computational and memory overhead. To address these challenges, we propose a set of optimizations, which includes GPU/ASIC acceleration, an efficient activation function, and an optimized packing scheme. We evaluate our method using the ResNet models on the CIFAR-10 and ImageNet datasets, achieving several orders of magnitude improvement compared to prior work and reducing the latency of the encrypted CNN inference to 1.4 seconds on an NVIDIA A100 GPU. We also show that the latency drops to a mere 0.03 seconds with a custom hardware design.
fields
cs.CR 3years
2026 3representative citing papers
A Numba CUDA-Python library for non-RNS CKKS-BGV on GPUs reaches latency comparable to CPU C++ non-RNS FHE libraries with far lower implementation complexity.
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.
citing papers explorer
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Beyond Latency: A System-Level Characterization of MPC and FHE for PPML
System-level evaluation reveals that network constraints and hardware costs, rather than raw latency, often dictate the optimal choice between MPC and FHE for privacy-preserving ML.
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LibFHE: A Numba-Based CUDA-Python Library for Non-RNS CKKS-BGV Fully Homomorphic Encryption on GPUs
A Numba CUDA-Python library for non-RNS CKKS-BGV on GPUs reaches latency comparable to CPU C++ non-RNS FHE libraries with far lower implementation complexity.
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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.