TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks

• URL: http://arxiv.org/abs/2504.19274v1
• Date: Sun, 27 Apr 2025 15:14:09 GMT
• Title: TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks
• Authors: Mohammad M Maheri, Hamed Haddadi, Alex Davidson,
• Abstract summary: ZK-SNARKs appear to provide the capability to verify model inference without access to sensitive data.<n>Applying ZK-SNARKs to modern neural networks, such as transformers and large vision models, introduces significant computational overhead.<n>We present TeleSparse, a ZK-friendly post-processing mechanisms to produce practical solutions to this problem.
• Score: 6.761197166421685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires access to model weights and (potentially sensitive or private) training data. So-called Zero-knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) would appear to provide the capability to verify model inference without access to such sensitive data. However, applying ZK-SNARKs to modern neural networks, such as transformers and large vision models, introduces significant computational overhead. We present TeleSparse, a ZK-friendly post-processing mechanisms to produce practical solutions to this problem. TeleSparse tackles two fundamental challenges inherent in applying ZK-SNARKs to modern neural networks: (1) Reducing circuit constraints: Over-parameterized models result in numerous constraints for ZK-SNARK verification, driving up memory and proof generation costs. We address this by applying sparsification to neural network models, enhancing proof efficiency without compromising accuracy or security. (2) Minimizing the size of lookup tables required for non-linear functions, by optimizing activation ranges through neural teleportation, a novel adaptation for narrowing activation functions' range. TeleSparse reduces prover memory usage by 67% and proof generation time by 46% on the same model, with an accuracy trade-off of approximately 1%. We implement our framework using the Halo2 proving system and demonstrate its effectiveness across multiple architectures (Vision-transformer, ResNet, MobileNet) and datasets (ImageNet,CIFAR-10,CIFAR-100). This work opens new directions for ZK-friendly model design, moving toward scalable, resource-efficient verifiable deep learning.

Related papers

• A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition [5.296139403757585]
  We propose an ultra-lightweight binary neural network (BNN) model designed for hardware deployment. The proposed model shows excellent recognition performance with an accuracy of up to 97.64%. Our research shows the great potential of BNN in the hardware deployment of computer vision models.
  arXiv  Detail & Related papers  (2025-01-14T03:19:10Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models [17.34908967455907]
  machine unlearning'' proposes the selective removal of unwanted data without the need for retraining from scratch. Fast-NTK is a novel NTK-based unlearning algorithm that significantly reduces the computational complexity.
  arXiv  Detail & Related papers  (2023-12-22T18:55:45Z)
• NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes [50.00272243518593]
  Deep neural networks (DNNs) have become ubiquitous in machine learning, but their energy consumption remains problematically high.<n>We have developed NeuralFuse, a novel add-on module that handles the energy-accuracy tradeoff in low-voltage regimes.<n>At a 1% bit-error rate, NeuralFuse can reduce access energy by up to 24% while recovering accuracy by up to 57%.
  arXiv  Detail & Related papers  (2023-06-29T11:38:22Z)
• NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction [37.357949900603295]
  We propose a neural architecture representation model that can be used to estimate attributes holistically. Experiment results show that our proposed framework can be used to predict the latency and accuracy attributes of both cell architectures and whole deep neural networks.
  arXiv  Detail & Related papers  (2022-11-15T10:15:21Z)
• A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification [9.365794791156972]
  This paper introduces a new Lightweight, Efficient and eXplainable-by-design convolutional neural network (LEXNet) for Internet traffic classification. LEXNet relies on a new residual block (for lightweight and efficiency purposes) and prototype layer (for explainability) Based on a commercial-grade dataset, our evaluation shows that LEXNet succeeds to maintain the same accuracy as the best performing state-of-the-art neural network.
  arXiv  Detail & Related papers  (2022-02-11T10:21:34Z)
• ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training [68.63354877166756]
  ActNN is a memory-efficient training framework that stores randomly quantized activations for back propagation. ActNN reduces the memory footprint of the activation by 12x, and it enables training with a 6.6x to 14x larger batch size.
  arXiv  Detail & Related papers  (2021-04-29T05:50:54Z)
• Binary Graph Neural Networks [69.51765073772226]
  Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. In this paper, we present and evaluate different strategies for the binarization of graph neural networks. We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy on challenging benchmarks.
  arXiv  Detail & Related papers  (2020-12-31T18:48:58Z)
• Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming [97.40955121478716]
  We propose a first-order dual SDP algorithm that requires memory only linear in the total number of network activations. We significantly improve L-inf verified robust accuracy from 1% to 88% and 6% to 40% respectively. We also demonstrate tight verification of a quadratic stability specification for the decoder of a variational autoencoder.
  arXiv  Detail & Related papers  (2020-10-22T12:32:29Z)
• Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments [24.674034243725455]
  Visual place recognition (VPR) is a robot's ability to determine whether a place was visited before using visual data. CNN-based approaches are unsuitable for resource-constrained platforms, such as small robots and drones. We propose a new class of highly compact models that drastically reduces the memory requirements and computational effort.
  arXiv  Detail & Related papers  (2020-10-01T22:59:34Z)
• Widening and Squeezing: Towards Accurate and Efficient QNNs [125.172220129257]
  Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters. Most of existing methods aim to enhance performance of QNNs especially binary neural networks by exploiting more effective training techniques. We address this problem by projecting features in original full-precision networks to high-dimensional quantization features.
  arXiv  Detail & Related papers  (2020-02-03T04:11:13Z)
• Lightweight Residual Densely Connected Convolutional Neural Network [18.310331378001397]
  The lightweight residual densely connected blocks are proposed to guaranty the deep supervision, efficient gradient flow, and feature reuse abilities of convolutional neural network. The proposed method decreases the cost of training and inference processes without using any special hardware-software equipment.
  arXiv  Detail & Related papers  (2020-01-02T17:15:32Z)

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.