# Secure Human Action Recognition by Encrypted Neural Network Inference

arXiv: https://arxiv.org/abs/2104.09164 · HTML: https://mlsanity.com/paper/2104.09164/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2021.

## Built on by (most cited first, 10 of 10)

- [AutoReP: Automatic ReLU Replacement for Fast Private Network Inference](https://mlsanity.com/paper/2308.10134/) (2023)
- [LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference](https://mlsanity.com/paper/2309.14331/) (2023)
- [Deep Learning Approaches for Seizure Video Analysis: A Review](https://mlsanity.com/paper/2312.10930/) (2023)
- [AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems](https://mlsanity.com/paper/2604.03425/) (2026)
- [ArctyrEX : Accelerated Encrypted Execution of General-Purpose Applications](https://mlsanity.com/paper/2306.11006/) (2023)
- [Secure and Efficient UAV-Based Face Detection via Homomorphic Encryption and Edge Computing](https://mlsanity.com/paper/2507.09860/) (2025)
- [Camera-based implicit mind reading by capturing higher-order semantic dynamics of human gaze within environmental context](https://mlsanity.com/paper/2507.12889/) (2025)
- [VIFSS: View-Invariant and Figure Skating-Specific Pose Representation Learning for Temporal Action Segmentation](https://mlsanity.com/paper/2508.10281/) (2025)
- [CFE-PPAR: Compression-friendly encryption for privacy-preserving action recognition leveraging video transformers](https://mlsanity.com/paper/2605.05692/) (2026)
- [FEnc2: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encoding](https://mlsanity.com/paper/2606.16359/) (2026)

## Abstract (arXiv)

Advanced computer vision technology can provide near real-time home monitoring to support "aging in place" by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average inference latency of 1.2 seconds and 2.4 seconds on real-world test datasets using small and large neural nets, respectively. We show that our method enables a 613x speedup over the latency-optimized LoLa and achieves an average of 3.1x throughput increase in secure inference compared to the throughput-optimized nGraph-HE2.