# Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study

arXiv: https://arxiv.org/abs/2411.06613 · HTML: https://mlsanity.com/paper/2411.06613/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 87 references that are in the map.
- First posted on arXiv 10 Nov 2024.

## Builds on (most cited first, 17 of 17)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Membership Inference Attacks From First Principles](https://mlsanity.com/paper/2112.03570/) (2021)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models](https://mlsanity.com/paper/1806.01246/) (2018)
- [Opacus: User-Friendly Differential Privacy Library in PyTorch](https://mlsanity.com/paper/2109.12298/) (2021)
- [Systematic Evaluation of Privacy Risks of Machine Learning Models](https://mlsanity.com/paper/2003.10595/) (2020)
- [Do CIFAR-10 Classifiers Generalize to CIFAR-10?](https://mlsanity.com/paper/1806.00451/) (2018)
- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [Low-Cost High-Power Membership Inference Attacks](https://mlsanity.com/paper/2312.03262/) (2023)
- [An Overview of Privacy in Machine Learning](https://mlsanity.com/paper/2005.08679/) (2020)
- [Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters](https://mlsanity.com/paper/2012.05321/) (2020)
- [NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips](https://mlsanity.com/paper/2005.08041/) (2020)
- [Efficient Privacy-Preserving Convolutional Spiking Neural Networks with FHE](https://mlsanity.com/paper/2309.09025/) (2023)
- [The Case for RISP: A Reduced Instruction Spiking Processor](https://mlsanity.com/paper/2206.14016/) (2022)
- [Functional Specification of the RAVENS Neuroprocessor](https://mlsanity.com/paper/2307.15232/) (2023)

## Built on by (most cited first, 5 of 5)

- [On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levels](https://mlsanity.com/paper/2502.18623/) (2025)
- [Do Spikes Protect Privacy? Investigating Black-Box Model Inversion Attacks in Spiking Neural Networks](https://mlsanity.com/paper/2502.05509/) (2025)
- [Privacy in Federated Learning with Spiking Neural Networks](https://mlsanity.com/paper/2511.21181/) (2025)
- [Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks](https://mlsanity.com/paper/2505.04034/) (2025)
- [Emerging Threats and Countermeasures in Neuromorphic Systems: A Survey](https://mlsanity.com/paper/2601.16589/) (2026)

## Abstract (arXiv)

While machine learning (ML) models are becoming mainstream, especially in sensitive application areas, the risk of data leakage has become a growing concern. Attacks like membership inference (MIA) have shown that trained models can reveal sensitive data, jeopardizing confidentiality. While traditional Artificial Neural Networks (ANNs) dominate ML applications, neuromorphic architectures, specifically Spiking Neural Networks (SNNs), are emerging as promising alternatives due to their low power consumption and event-driven processing, akin to biological neurons. Privacy in ANNs is well-studied; however, little work has explored the privacy-preserving properties of SNNs. This paper examines whether SNNs inherently offer better privacy. Using MIAs, we assess the privacy resilience of SNNs versus ANNs across diverse datasets. We analyze the impact of learning algorithms (surrogate gradient and evolutionary), frameworks (snnTorch, TENNLab, LAVA), and parameters on SNN privacy. Our findings show that SNNs consistently outperform ANNs in privacy preservation, with evolutionary algorithms offering additional resilience. For instance, on CIFAR-10, SNNs achieve an AUC of 0.59, significantly lower than ANNs' 0.82, and on CIFAR-100, SNNs maintain an AUC of 0.58 compared to ANNs' 0.88. Additionally, we explore the privacy-utility trade-off with Differentially Private Stochastic Gradient Descent (DPSGD), finding that SNNs sustain less accuracy loss than ANNs under similar privacy constraints.