CrypTFlow2: Practical 2-Party Secure Inference

• URL: http://arxiv.org/abs/2010.06457v1
• Date: Tue, 13 Oct 2020 15:12:28 GMT
• Title: CrypTFlow2: Practical 2-Party Secure Inference
• Authors: Deevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
• Abstract summary: CrypTFlow2 is a cryptographic framework for secure inference over realistic Deep Neural Networks (DNNs) We present the first secure inference over ImageNet-scale DNNs like ResNet50 and DenseNet121. CrypTFlow2 requires an order of magnitude less communication and 20x-30x less time than the state-of-the-art.
• Score: 10.733878947770283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both correct -- i.e., their outputs are bitwise equivalent to the cleartext execution -- and efficient -- they outperform the state-of-the-art protocols in both latency and scale. At the core of CrypTFlow2, we have new 2PC protocols for secure comparison and division, designed carefully to balance round and communication complexity for secure inference tasks. Using CrypTFlow2, we present the first secure inference over ImageNet-scale DNNs like ResNet50 and DenseNet121. These DNNs are at least an order of magnitude larger than those considered in the prior work of 2-party DNN inference. Even on the benchmarks considered by prior work, CrypTFlow2 requires an order of magnitude less communication and 20x-30x less time than the state-of-the-art.

Related papers

• TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals [58.865901821451295]
  We present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form.
  arXiv  Detail & Related papers  (2024-04-15T06:01:48Z)
• Optimizing data-flow in Binary Neural Networks [0.0]
  We propose a novel training scheme that can increase data flow and parallelism in the BNN pipeline. We also present an optimized implementation of the Binary Direct Convolution for ARM instruction sets. Our experiments show a consistent improvement of the inference speed (up to 1.91 and 2.73x compared to two state-of-the-art BNNs frameworks) with no drop in accuracy for at least one full-precision model.
  arXiv  Detail & Related papers  (2023-04-03T13:16:33Z)
• BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance [54.214426436283134]
  Deep neural networks, such as the Deep-FSMN, have been widely studied for keyword spotting (KWS) applications. We present a strong yet efficient binary neural network for KWS, namely BiFSMNv2, pushing it to the real-network accuracy performance. We highlight that benefiting from the compact architecture and optimized hardware kernel, BiFSMNv2 can achieve an impressive 25.1x speedup and 20.2x storage-saving on edge hardware.
  arXiv  Detail & Related papers  (2022-11-13T18:31:45Z)
• AEGNN: Asynchronous Event-based Graph Neural Networks [54.528926463775946]
  Event-based Graph Neural Networks generalize standard GNNs to process events as "evolving"-temporal graphs. AEGNNs are easily trained on synchronous inputs and can be converted to efficient, "asynchronous" networks at test time.
  arXiv  Detail & Related papers  (2022-03-31T16:21:12Z)
• Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration [83.84684675841167]
  We propose a novel encoding scheme using -1, +1 to decompose quantized neural networks (QNNs) into multi-branch binary networks. We validate the effectiveness of our method on large-scale image classification, object detection, and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2021-06-18T03:11:15Z)
• SIRNN: A Math Library for Secure RNN Inference [6.323743920987275]
  We provide new specialized 2PC protocols for math functions that crucially rely on lookup-tables and mixed-bitwidths. Our protocols for math functions communicate up to 423x less data than prior work. We build on top of our novel protocols to build SIRNN, a library for end-to-end secure 2-party inference.
  arXiv  Detail & Related papers  (2021-05-10T10:04:46Z)
• SoFAr: Shortcut-based Fractal Architectures for Binary Convolutional Neural Networks [7.753767947048147]
  We propose two Shortcut-based Fractal Architectures (SoFAr) specifically designed for Binary Convolutional Neural Networks (BCNNs) Our proposed SoFAr combines the adoption of shortcuts and the fractal architectures in one unified model, which is helpful in the training of BCNNs. Results show that our proposed SoFAr achieves better accuracy compared with shortcut-based BCNNs.
  arXiv  Detail & Related papers  (2020-09-11T10:00:47Z)
• FATNN: Fast and Accurate Ternary Neural Networks [89.07796377047619]
  Ternary Neural Networks (TNNs) have received much attention due to being potentially orders of magnitude faster in inference, as well as more power efficient, than full-precision counterparts. In this work, we show that, under some mild constraints, computational complexity of the ternary inner product can be reduced by a factor of 2. We elaborately design an implementation-dependent ternary quantization algorithm to mitigate the performance gap.
  arXiv  Detail & Related papers  (2020-08-12T04:26:18Z)
• Term Revealing: Furthering Quantization at Run Time on Quantized DNNs [9.240133036531402]
  We present a novel technique, called Term Revealing (TR), for furthering quantization at run time for improved performance of Deep Neural Networks (DNNs) already quantized with conventional quantization methods. TR operates on power-of-two terms in binary expressions of values. We show an FPGA implementation that can use a small number of control bits to switch between conventional quantization and TR-enabled quantization with a negligible delay.
  arXiv  Detail & Related papers  (2020-07-13T14:03:10Z)
• Distillation Guided Residual Learning for Binary Convolutional Neural Networks [83.6169936912264]
  It is challenging to bridge the performance gap between Binary CNN (BCNN) and Floating point CNN (FCNN) We observe that, this performance gap leads to substantial residuals between intermediate feature maps of BCNN and FCNN. To minimize the performance gap, we enforce BCNN to produce similar intermediate feature maps with the ones of FCNN. This training strategy, i.e., optimizing each binary convolutional block with block-wise distillation loss derived from FCNN, leads to a more effective optimization to BCNN.
  arXiv  Detail & Related papers  (2020-07-10T07:55:39Z)

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.