mlsanityData to 14 Oct 2026

DPSNN: A Differentially Private Spiking Neural Network with Temporal Enhanced Pooling

arXiv 2205.12718 ↗First posted 24 May 2022cs.NE cs.AI cs.LGcited by 1

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Abstract (arXiv)

Privacy protection is a crucial issue in machine learning algorithms, and the current privacy protection is combined with traditional artificial neural networks based on real values. Spiking neural network (SNN), the new generation of artificial neural networks, plays a crucial role in many fields. Therefore, research on the privacy protection of SNN is urgently needed. This paper combines the differential privacy(DP) algorithm with SNN and proposes a differentially private spiking neural network (DPSNN). The SNN uses discrete spike sequences to transmit information, combined with the gradient noise introduced by DP so that SNN maintains strong privacy protection. At the same time, to make SNN maintain high performance while obtaining high privacy protection, we propose the temporal enhanced pooling (TEP) method. It fully integrates the temporal information of SNN into the spatial information transfer, which enables SNN to perform better information transfer. We conduct experiments on static and neuromorphic datasets, and the experimental results show that our algorithm still maintains high performance while providing strong privacy protection.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 56 references that are in the map. First posted on arXiv 24 May 2022.

Builds on

32 of 56 references.

References · 32

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  2. 2016Deep Learning with Differential Privacycited by 2,728
  3. 2018Group Normalizationcited by 1,524
  4. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,198
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  10. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 406
  11. 2021Opacus: User-Friendly Differential Privacy Library in PyTorchcited by 383
  12. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 334
20 more
  1. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 314
  2. 2018Differentially Private Generative Adversarial Networkcited by 235
  3. 2020Differentially Private Learning Needs Better Features (or Much More Data)cited by 235
  4. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  5. 2019Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learningcited by 122
  6. 2020Tempered Sigmoid Activations for Deep Learning with Differential Privacycited by 122
  7. 2019Deep Learning with Gaussian Differential Privacycited by 114
  8. 2014Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Boundscited by 107
  9. 2020Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networkscited by 100
  10. 2020Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratchcited by 80
  11. 2018A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rulecited by 36
  12. 2017Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learningcited by 34
  13. 2018Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networkscited by 30
  14. 2018I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Acceleratorscited by 27
  15. 2021Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networkscited by 23
  16. 2019Privacy-Preserving Gradient Boosting Decision Treescited by 20
  17. 2019P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classificationcited by 14
  18. 2021BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neuronscited by 12
  19. 2020Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryptioncited by 7
  20. 2020On the effect of normalization layers on Differentially Private training of deep Neural networkscited by 6

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levelscited by 2