PrivCirNet: Efficient Private Inference via Block Circulant Transformation

• URL: http://arxiv.org/abs/2405.14569v3
• Date: Tue, 29 Oct 2024 02:20:24 GMT
• Title: PrivCirNet: Efficient Private Inference via Block Circulant Transformation
• Authors: Tianshi Xu, Lemeng Wu, Runsheng Wang, Meng Li,
• Abstract summary: Homomorphic encryption (HE)-based deep neural network (DNN) inference protects data and model privacy but suffers from significant computation overhead. We propose PrivCirNet, a protocol/network co-optimization framework based on block circulant transformation. PrivCirNet customizes the HE encoding algorithm that is fully compatible with the block circulant transformation.
• Score: 11.859511840002916
• License:
• Abstract: Homomorphic encryption (HE)-based deep neural network (DNN) inference protects data and model privacy but suffers from significant computation overhead. We observe transforming the DNN weights into circulant matrices converts general matrix-vector multiplications into HE-friendly 1-dimensional convolutions, drastically reducing the HE computation cost. Hence, in this paper, we propose \method, a protocol/network co-optimization framework based on block circulant transformation. At the protocol level, PrivCirNet customizes the HE encoding algorithm that is fully compatible with the block circulant transformation and reduces the computation latency in proportion to the block size. At the network level, we propose a latency-aware formulation to search for the layer-wise block size assignment based on second-order information. PrivCirNet also leverages layer fusion to further reduce the inference cost. We compare PrivCirNet with the state-of-the-art HE-based framework Bolt (IEEE S\&P 2024) and the HE-friendly pruning method SpENCNN (ICML 2023). For ResNet-18 and Vision Transformer (ViT) on Tiny ImageNet, PrivCirNet reduces latency by $5.0\times$ and $1.3\times$ with iso-accuracy over Bolt, respectively, and improves accuracy by $4.1\%$ and $12\%$ over SpENCNN, respectively. For MobileNetV2 on ImageNet, PrivCirNet achieves $1.7\times$ lower latency and $4.2\%$ better accuracy over Bolt and SpENCNN, respectively. Our code and checkpoints are available on Git Hub.

Related papers

• HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference [2.498379184732383]
  We propose HEQuant, which features low-precision-quantization-aware optimization for the HE-based protocols. Compared with prior-art HE-based protocols, e.g., CrypTFlow2, Cheetah, Iron, etc, HEQuant achieves $3.5sim 23.4times$ communication reduction.
  arXiv  Detail & Related papers  (2024-01-29T08:59:05Z)
• Toward Practical Privacy-Preserving Convolutional Neural Networks Exploiting Fully Homomorphic Encryption [11.706881389387242]
  Homomorphic encryption (FHE) is a viable approach for achieving private inference (PI) FHE implementation of a CNN faces significant hurdles, primarily due to FHE's substantial computational and memory overhead. We propose a set of optimizations, which includes GPU/ASIC acceleration, an efficient activation function, and an optimized packing scheme.
  arXiv  Detail & Related papers  (2023-10-25T10:24:35Z)
• Instant Complexity Reduction in CNNs using Locality-Sensitive Hashing [50.79602839359522]
  We propose HASTE (Hashing for Tractable Efficiency), a parameter-free and data-free module that acts as a plug-and-play replacement for any regular convolution module. We are able to drastically compress latent feature maps without sacrificing much accuracy by using locality-sensitive hashing (LSH) In particular, we are able to instantly drop 46.72% of FLOPs while only losing 1.25% accuracy by just swapping the convolution modules in a ResNet34 on CIFAR-10 for our HASTE module.
  arXiv  Detail & Related papers  (2023-09-29T13:09:40Z)
• DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Transformers [105.74546828182834]
  We show a hardware-efficient dynamic inference regime, named dynamic weight slicing, which adaptively slice a part of network parameters for inputs with diverse difficulty levels. We present dynamic slimmable network (DS-Net) and dynamic slice-able network (DS-Net++) by input-dependently adjusting filter numbers of CNNs and multiple dimensions in both CNNs and transformers.
  arXiv  Detail & Related papers  (2021-09-21T09:57:21Z)
• OMPQ: Orthogonal Mixed Precision Quantization [64.59700856607017]
  Mixed precision quantization takes advantage of hardware's multiple bit-width arithmetic operations to unleash the full potential of network quantization. We propose to optimize a proxy metric, the concept of networkity, which is highly correlated with the loss of the integer programming. This approach reduces the search time and required data amount by orders of magnitude, with little compromise on quantization accuracy.
  arXiv  Detail & Related papers  (2021-09-16T10:59:33Z)
• HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture [16.934772841669275]
  Homomorphic Encryption (HE) is used to implement Privacy-Preserving Neural Networks (PPNNs) We propose a textbfHE-friendly privacy-preserving textbfMobile neural ntextbfETwork architecture, textbfHEMET.
  arXiv  Detail & Related papers  (2021-05-31T18:05:53Z)
• 1$\times$N Block Pattern for Network Sparsity [90.43191747596491]
  We propose one novel concept of $1times N$ block sparsity pattern (block pruning) to break this limitation. Our pattern obtains about 3.0% improvements over filter pruning in the top-1 accuracy of MobileNet-V2. It also obtains 56.04ms inference savings on Cortex-A7 CPU over weight pruning.
  arXiv  Detail & Related papers  (2021-05-31T05:50:33Z)
• Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets [65.28292822614418]
  Giant formula for simultaneously enlarging the resolution, depth and width provides us a Rubik's cube for neural networks. This paper aims to explore the twisting rules for obtaining deep neural networks with minimum model sizes and computational costs.
  arXiv  Detail & Related papers  (2020-10-28T08:49:45Z)
• Efficient Integer-Arithmetic-Only Convolutional Neural Networks [87.01739569518513]
  We replace conventional ReLU with Bounded ReLU and find that the decline is due to activation quantization. Our integer networks achieve equivalent performance as the corresponding FPN networks, but have only 1/4 memory cost and run 2x faster on modern GPU.
  arXiv  Detail & Related papers  (2020-06-21T08:23:03Z)
• Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations [22.71924873981158]
  Precision gating (PG) is an end-to-end trainable dynamic dual-precision quantization technique for deep neural networks. PG achieves excellent results on CNNs, including statically compressed mobile-friendly networks such as ShuffleNet. Compared to 8-bit uniform quantization, PG obtains a 1.2% improvement in perplexity per word with 2.7$times$ computational cost reduction on LSTM.
  arXiv  Detail & Related papers  (2020-02-17T18:54:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.