mlsanityData to 7 Oct 2026

On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis

arXiv 2502.13191 ↗First posted 18 Feb 2025cs.LG cs.AIcited by 1

On the map: LLM safety and security → Membership inference attacks

Abstract (arXiv)

Spiking Neural Networks (SNNs) are increasingly explored for their energy efficiency and robustness in real-world applications, yet their privacy risks remain largely unexamined. In this work, we investigate the susceptibility of SNNs to Membership Inference Attacks (MIAs) -- a major privacy threat where an adversary attempts to determine whether a given sample was part of the training dataset. While prior work suggests that SNNs may offer inherent robustness due to their discrete, event-driven nature, we find that its resilience diminishes as latency (T) increases. Furthermore, we introduce an input dropout strategy under black box setting, that significantly enhances membership inference in SNNs. Our findings challenge the assumption that SNNs are inherently more secure, and even though they are expected to be better, our results reveal that SNNs exhibit privacy vulnerabilities that are equally comparable to Artificial Neural Networks (ANNs). Our code is available at https://github.com/sharmaabhijith/MIA_SNN.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Membership inference attacks, one of 12 directions in LLM safety and security. Builds on 33 of its 56 references that are in the map. First posted on arXiv 18 Feb 2025.

Builds on

33 of 56 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023Low-Cost High-Power Membership Inference Attacks6 Dec
  2. 2021Membership Inference Attacks From First Principles7 Dec
  3. 2021Enhanced Membership Inference Attacks against Machine Learning Models18 Nov
  4. 2021Membership Inference Attacks on Machine Learning: A Survey14 Mar

Other references · 29

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  5. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  6. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  7. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  8. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 475
  9. 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 427
  10. 2021A Survey of Transformerscited by 399
  11. 2018Deep Learning in Spiking Neural Networkscited by 258
  12. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 192
17 more
  1. 2018Long short-term memory and learning-to-learn in networks of spiking neuronscited by 179
  2. 2020A Survey of Privacy Attacks in Machine Learningcited by 143
  3. 2020Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagationcited by 142
  4. 2021DEUP: Direct Epistemic Uncertainty Predictioncited by 102
  5. 2022Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representationcited by 89
  6. 2023Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipelinecited by 50
  7. 2019A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networkscited by 35
  8. 2020Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameterscited by 27
  9. 2023Learning Long Sequences in Spiking Neural Networkscited by 19
  10. 2021PrivateSNN: Privacy-Preserving Spiking Neural Networkscited by 15
  11. 2022Out of Distribution Data Detection Using Dropout Bayesian Neural Networkscited by 15
  12. 2022Sub-mW Neuromorphic SNN audio processing applications with Rockpool and Xylocited by 12
  13. 2023Applications of Spiking Neural Networks in Visual Place Recognitioncited by 9
  14. 2023Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropoutcited by 5
  15. 2022Analysis of Power-Oriented Fault Injection Attacks on Spiking Neural Networkscited by 4
  16. 2023Brain-Inspired Spiking Neural Networks for Industrial Fault Diagnosis: A Survey, Challenges, and Opportunitiescited by 4
  17. 2024Membership Privacy Evaluation in Deep Spiking Neural Networkscited by 3

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Privacy in Federated Learning with Spiking Neural Networkscited by 1