Privacy-Preserving Neuromorphic Computation

• Privacy-preserving neuromorphic computation is a paradigm using event-driven, asynchronous neural architectures that process only significant spiking events to minimize sensitive data exposure.
• Advanced hardware and algorithmic mechanisms, such as event-based sensors and spiking neural networks, reduce data volume and mitigate reconstruction risks through on-chip anonymization.
• Empirical benchmarks and cryptographic integrations demonstrate that these systems achieve high accuracy and energy efficiency while safeguarding privacy in real-world applications.

Neuromorphic computation, characterized by event-driven, asynchronous information processing that emulates biological neural systems, exhibits distinctive privacy-preserving potential across sensing, data processing, and learning. This promise arises from both the hardware modality—especially event-based sensors and spiking neural architectures—and algorithmic properties, but is subject to nuanced caveats depending on deployment context and threat model.

1. Principles of Privacy-Preserving Neuromorphic Sensing

Event-based vision—exemplified by the Sony IMX636 event camera (1280×720 px, per-pixel μs time resolution)—detects only significant local changes in log-brightness, emitting sparse events $e_i = (x_i, y_i, t_i, p_i)$ when a pixel’s intensity change exceeds a threshold. No absolute intensity, color, or static scene is recorded. Static backgrounds, face details, and identifying features are never present in the primary sensor data, in contrast to frame-based imaging (Khacef et al., 27 Nov 2025, Dong et al., 2023).

In applications such as privacy-aware surveillance or eldercare fall detection, this hardware-level data minimization ensures:

• Data volumes are drastically reduced (e.g., 1 M ev/s vs. >100 MB/s for video)
• Only transient edges and changes persist, impeding efforts to reconstruct detailed identity information or backgrounds
• All processing can remain on-device, supporting immediate event discarding—never exposing raw events off-board

However, it is critical to note that even event streams, if intercepted, may permit partial signal inversion, motivating on-chip anonymization or encryption (Khacef et al., 27 Nov 2025, Zhang et al., 2023).

2. Neuromorphic Architectures: Hardware and Algorithmic Substrate

Advanced architectures, such as those utilizing the Intel Loihi 2 neuromorphic processor (128 asynchronous neuro-cores, graded spikes, near-memory compute), further these privacy goals by tightly coupling event-driven dataflow with massively parallel, sparse, and energy-efficient spiking neural networks (SNNs) (Khacef et al., 27 Nov 2025).

Core system-level design choices that support privacy include:

• FPGA-based interfaces performing local region-of-interest cropping, down-sampling, and mapping events to algorithmic timesteps before encoding as graded spikes
• No construction or buffering of high-resolution image frames in memory; only spike or temporally-local feature vectors reside on-chip
• S4D state space models and MCUNet feature extractors with patched inference minimize intermediate compute state and eliminate the need to store frame, feature, or attention buffers
• Event-spike routing and computation performed in near-memory SRAM, fully avoiding off-chip DRAM transfers

The result is that, even for high-functionality tasks such as always-on fall detection, the chip can achieve high F1 scores (e.g., 84% at sub-100 mW) without ever assembling or exposing privacy-sensitive representations (Khacef et al., 27 Nov 2025).

3. Quantitative Models, Trade-offs, and Empirical Results

Neuromorphic privacy and utility must be quantitatively situated using explicit models and benchmarks:

Model Type	F1 Score	SynOps Sparsity	Power (mW)
CNN+MLP (graded LIF)	58.1%	1/55	46
CNN+S4D	76.9%	1/2.9	77
MCUNet₁₃B+S4D (patched inf.)	83.6%	1/2	89

The circuit-level sparsity factor ($S = N_{\mathrm{active}} / N_{\text{max}}$) quantifies event-driven efficiency, and power modeling incorporates static and dynamic terms as $$P_{\text{inf}} = P_{\text{static}} + \eta_{\text{syn}} N_{\text{SynOps/s}} + \eta_{\text{spk}} N_{\text{spikes/s}}$$ (Khacef et al., 27 Nov 2025).

Empirical findings from real-world deployments demonstrate that:

• Privacy is preserved by never constructing full images or exporting event data
• Always-on edge AI becomes feasible within a sub-100 mW envelope, even with high model accuracy
• Graded spikes—in contrast to binary—offer further privacy/utility improvement by reducing spike activity and SynOp count for a given detection score

4. Attacks, Privacy Limitations, and Mitigations in Neuromorphic Learning

Despite architectural advantages, neuromorphic systems are not categorically immune to data leakage. Several modern attacks have been adapted:

• Membership Inference Attacks (MIA): SNNs often offer lower leakage than ANNs (e.g., AUC drops from 0.82 to 0.59 on CIFAR-10), especially when trained with evolutionary or STDP-based learning and under DPSGD. But on event-driven datasets, SNNs can suffer higher MIA attack rates (up to 10% over ANNs), depending on generalization gap, membrane dynamics, and hyperparameters (Moshruba et al., 10 Nov 2024, Li et al., 28 Sep 2024).
• Gradient Inversion in Federated Learning: SNNs trained via surrogate gradients leak less informative gradients—producing temporally noisy reconstructions with much lower attack success rates (DLG ASR drops: 79.4% → 37.2% on MNIST). Noisy surrogate backpropagation and temporal information spreading both contribute to diminished attack fidelity (Aksu et al., 26 Nov 2025).
• Model Inversion Attacks: Non-differentiability, sparsity, and quantization add friction to direct inversion, but novel attacks (e.g., population-based Bernoulli optimization) adaptively reconstruct inputs from SNNs, sometimes with surprising efficacy—especially on low-complexity static data—questioning the notion of inherent SNN privacy (Poursiami et al., 1 Feb 2024).

Data augmentation, threshold jittering, randomized event-drop, and temporal-spatial distortions (NDA, EventDrop) offer practical on-chip defenses, reducing attack accuracy by up to 25.7% at modest utility costs (Li et al., 28 Sep 2024).

5. Cryptographic Enhancement: Homomorphic Encryption for SNNs

For formal, cryptographically-strong privacy, several frameworks integrate homomorphic encryption (HE) with deep SNNs:

• CKKS-based PRIVSPIKE supports deep SNNs (LeNet-5, ResNet-19) under encrypted inference, with options for LIF polynomial approximation (Chebyshev degree 50) or scheme-switching to TFHE for exact thresholding. Encrypted accuracy loss is ≤1%, but with 10–10³× runtime overhead (Njungle et al., 5 Oct 2025).
• BFV-based SNN inference demonstrates that, for tight modulus budgets, SNNs outperform DNNs by up to 40% in encrypted classification accuracy, owing to robust temporal-spike averaging (Nikfam et al., 2023).
• FHE-DiSNN/FHE-DiCSNN (TFHE) achieves >95% accuracy on MNIST under full FHE, leveraging SNNs’ binary spike outputs to avoid deep polynomial approximations required for DNN activations. Homomorphic implementations of Fire and Reset via programmable bootstrapping facilitate arbitrary-depth SNNs under ciphertext, with parallelized evaluation yielding subsecond encrypted inference (Li et al., 2023, Li et al., 2023).
• Event Encryption for Sensor Data: Carefully designed pseudo-noise injection obfuscates event streams at the output of neuromorphic cameras, preventing both low-level (reconstruction) and high-level (classification, detection) leakage while maintaining reversible decryption for authorized parties (Zhang et al., 2023).

These cryptographic approaches are threat-model robust, eliminating reliance on obscurity or “inherent” privacy, and are applicable to both inference and offloading in distributed/cloud neuromorphic systems.

6. Open Questions and Future Directions

Key open research directions include:

• Sensor-Processor Co-design: Jointly tune event-trigger thresholds, ROI, and neuromorphic inference pipelines for minimal privacy exposure and maximal task utility (Khacef et al., 27 Nov 2025).
• On-chip Anonymization: Develop lightweight, low-latency mechanisms for event-level anonymization (differential privacy, spatial jitter) that operate in streaming mode without degrading accuracy or energy efficiency.
• Formal Privacy Metrics: Move beyond size or information-theoretic proxies toward quantifying reconstructibility and attack resilience in spike-generated representations.
• Trade-offs: Optimal privacy–utility curves require further paper, especially as SNN and event-camera pipelines scale to high-resolution (ImageNet-scale) vision or complex multimodal sensing.
• Full-stack Zero-Trust Architectures: Architect asynchronous, event-driven systems where only analytics results—never raw or reconstructed signals—are extractable from the device.

Neuromorphic computation thus forms a privileged substrate for privacy-by-design AI, but achieving provable guarantees in real world deployments demands integrating hardware sparsity, algorithmic nonlinearity, cryptographic encapsulation, and context-aware system co-optimization across the entire dataflow (Khacef et al., 27 Nov 2025, Zhang et al., 2023, Njungle et al., 5 Oct 2025).