Batch-oriented Element-wise Approximate Activation for Privacy-Preserving Neural Networks

• URL: http://arxiv.org/abs/2403.10920v1
• Date: Sat, 16 Mar 2024 13:26:33 GMT
• Title: Batch-oriented Element-wise Approximate Activation for Privacy-Preserving Neural Networks
• Authors: Peng Zhang, Ao Duan, Xianglu Zou, Yuhong Liu,
• Abstract summary: Homomorphic Encryption (FHE) is facing a great challenge that homomorphic operations cannot be easily adapted for non-linear activation calculations. Batch-oriented element-wise data packing and approximate activation are proposed, which train linear low-degrees to approximate the non-linear activation function - ReLU. Experiment results show that when ciphertext inference is performed on 4096 input images, compared with the current most efficient channel-wise method, the inference accuracy is improved by 1.65%, and the amortized inference time is reduced by 99.5%.
• Score: 5.039738753594332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Privacy-Preserving Neural Networks (PPNN) are advanced to perform inference without breaching user privacy, which can serve as an essential tool for medical diagnosis to simultaneously achieve big data utility and privacy protection. As one of the key techniques to enable PPNN, Fully Homomorphic Encryption (FHE) is facing a great challenge that homomorphic operations cannot be easily adapted for non-linear activation calculations. In this paper, batch-oriented element-wise data packing and approximate activation are proposed, which train linear low-degree polynomials to approximate the non-linear activation function - ReLU. Compared with other approximate activation methods, the proposed fine-grained, trainable approximation scheme can effectively reduce the accuracy loss caused by approximation errors. Meanwhile, due to element-wise data packing, a large batch of images can be packed and inferred concurrently, leading to a much higher utility ratio of ciphertext slots. Therefore, although the total inference time increases sharply, the amortized time for each image actually decreases, especially when the batch size increases. Furthermore, knowledge distillation is adopted in the training process to further enhance the inference accuracy. Experiment results show that when ciphertext inference is performed on 4096 input images, compared with the current most efficient channel-wise method, the inference accuracy is improved by 1.65%, and the amortized inference time is reduced by 99.5%.

Related papers

• Disparate Impact on Group Accuracy of Linearization for Private Inference [48.27026603581436]
  We show that reducing the number of ReLU activations disproportionately decreases the accuracy for minority groups compared to majority groups. We also show how a simple procedure altering the fine-tuning step for linearized models can serve as an effective mitigation strategy.
  arXiv  Detail & Related papers  (2024-02-06T01:56:29Z)
• Variational Bayes image restoration with compressive autoencoders [4.879530644978008]
  Regularization of inverse problems is of paramount importance in computational imaging. In this work, we first propose to use compressive autoencoders instead of state-of-the-art generative models. As a second contribution, we introduce the Variational Bayes Latent Estimation (VBLE) algorithm.
  arXiv  Detail & Related papers  (2023-11-29T15:49:31Z)
• Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation [80.227864832092]
  Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse array of applications. The sheer size of large-scale graphs presents a significant challenge to real-time inference with GNNs. We propose an online propagation framework and two novel node-adaptive propagation methods.
  arXiv  Detail & Related papers  (2023-10-17T05:03:00Z)
• Guaranteed Approximation Bounds for Mixed-Precision Neural Operators [83.64404557466528]
  We build on intuition that neural operator learning inherently induces an approximation error. We show that our approach reduces GPU memory usage by up to 50% and improves throughput by 58% with little or no reduction in accuracy.
  arXiv  Detail & Related papers  (2023-07-27T17:42:06Z)
• dugMatting: Decomposed-Uncertainty-Guided Matting [83.71273621169404]
  We propose a decomposed-uncertainty-guided matting algorithm, which explores the explicitly decomposed uncertainties to efficiently and effectively improve the results. The proposed matting framework relieves the requirement for users to determine the interaction areas by using simple and efficient labeling.
  arXiv  Detail & Related papers  (2023-06-02T11:19:50Z)
• Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy [25.763777765222358]
  Federated learning (FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. As the model size grows, the training latency due to limited transmission bandwidth and private information degrades while using differential privacy (DP) protection. We propose sparsification empowered FL framework wireless channels, in over to improve training efficiency without sacrificing convergence performance.
  arXiv  Detail & Related papers  (2023-04-09T05:21:15Z)
• Reliable Prediction Intervals with Directly Optimized Inductive Conformal Regression for Deep Learning [3.42658286826597]
  Predictions intervals (PIs) are used to quantify the uncertainty of each prediction in deep learning regression. Many approaches to improve the quality of PIs can effectively reduce the width of PIs, but they do not ensure that enough real labels are captured. In this study, we use Directly Optimized Inductive Conformal Regression (DOICR) that takes only the average width of PIs as the loss function. Benchmark experiments show that DOICR outperforms current state-of-the-art algorithms for regression problems.
  arXiv  Detail & Related papers  (2023-02-02T04:46:14Z)
• Efficient Graph Neural Network Inference at Large Scale [54.89457550773165]
  Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications. Existing scalable GNNs leverage linear propagation to preprocess the features and accelerate the training and inference procedure. We propose a novel adaptive propagation order approach that generates the personalized propagation order for each node based on its topological information.
  arXiv  Detail & Related papers  (2022-11-01T14:38:18Z)
• Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations [57.20885629270732]
  We consider privacy aspects of wireless federated learning with Over-the-Air (OtA) transmission of gradient updates from multiple users/agents to an edge server. Traditional perturbation-based methods provide privacy protection while sacrificing the training accuracy. In this work, we aim at minimizing privacy leakage to the adversary and the degradation of model accuracy at the edge server.
  arXiv  Detail & Related papers  (2022-10-05T13:13:35Z)

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.