On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis
Guan, Junyi Sharma, Abhijith Tian, Chong Lahlou, Salem
Guan, Junyi
Sharma, Abhijith
Tian, Chong
Lahlou, Salem
Supervisor
Department
Machine Learning
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
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.
Citation
J. Guan, A. Sharma, C. Tian, and S. Lahlou, “On the privacy risks of spiking neural networks: A membership inference analysis,” in Proc. 41st Conf. Uncertainty in Artif. Intell. (UAI 2025), vol. 286, Proc. Mach. Learn. Res., Rio de Janeiro, Brazil, Jul. 21–25, 2025, pp. 1586–1597
Source
Proceedings of Machine Learning Research
Conference
UAI '25: Conference on Uncertainty in Artificial Intelligence
Keywords
Data Privacy, Differential Privacy, Energy Efficiency, Black Boxes, Discrete Event Driven, Energy, Inference Attacks, Neural-networks, Privacy Risks, Privacy Threats, Real-world, Training Dataset, Neural Networks
Subjects
Source
UAI '25: Conference on Uncertainty in Artificial Intelligence
Publisher
ML Research Press