# Anonymization for Skeleton Action Recognition

arXiv: https://arxiv.org/abs/2111.15129 · HTML: https://mlsanity.com/paper/2111.15129/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 38 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/1904.12659/) (2019)
- [An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1902.09130/) (2019)
- [DeepPrivacy: A Generative Adversarial Network for Face Anonymization](https://mlsanity.com/paper/1909.04538/) (2019)
- [CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks](https://mlsanity.com/paper/2005.09544/) (2020)
- [A Study of Face Obfuscation in ImageNet](https://mlsanity.com/paper/2103.06191/) (2021)
- [Towards Robust and Privacy-preserving Text Representations](https://mlsanity.com/paper/1805.06093/) (2018)
- [Mobile Sensor Data Anonymization](https://mlsanity.com/paper/1810.11546/) (2018)
- [Learning to Anonymize Faces for Privacy Preserving Action Detection](https://mlsanity.com/paper/1803.11556/) (2018)
- [Privacy-preserving Neural Representations of Text](https://mlsanity.com/paper/1808.09408/) (2018)
- [Adversarial Attack on Skeleton-based Human Action Recognition](https://mlsanity.com/paper/1909.06500/) (2019)
- [ETRI-Activity3D: A Large-Scale RGB-D Dataset for Robots to Recognize Daily Activities of the Elderly](https://mlsanity.com/paper/2003.01920/) (2020)
- [BASAR:Black-box Attack on Skeletal Action Recognition](https://mlsanity.com/paper/2103.05266/) (2021)
- [Password-conditioned Anonymization and Deanonymization with Face Identity Transformers](https://mlsanity.com/paper/1911.11759/) (2019)
- [Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack](https://mlsanity.com/paper/2103.05347/) (2021)
- [Privacy Protection in Street-View Panoramas using Depth and Multi-View Imagery](https://mlsanity.com/paper/1903.11532/) (2019)
- [Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition](https://mlsanity.com/paper/2005.07151/) (2020)
- [Adversarial Bone Length Attack on Action Recognition](https://mlsanity.com/paper/2109.05830/) (2021)
- [Automated Anonymisation of Visual and Audio Data in Classroom Studies](https://mlsanity.com/paper/2001.05080/) (2020)

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

- [TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection](https://mlsanity.com/paper/2308.11072/) (2023)
- [Adversary-Guided Motion Retargeting for Skeleton Anonymization](https://mlsanity.com/paper/2405.05428/) (2024)
- [Privacy-Preserving Semantic Segmentation from Ultra-Low-Resolution RGB Inputs](https://mlsanity.com/paper/2507.16034/) (2025)
- [GaitProtector: Impersonation-Driven Gait De-Identification via Training-Free Diffusion Latent Optimization](https://mlsanity.com/paper/2605.12431/) (2026)
- [Privacy-Preserving Action Recognition: Taxonomy, Methods, and Privacy-Utility Trade-offs](https://mlsanity.com/paper/2608.04501/) (2026)

## Abstract (arXiv)

Skeleton-based action recognition attracts practitioners and researchers due to the lightweight, compact nature of datasets. Compared with RGB-video-based action recognition, skeleton-based action recognition is a safer way to protect the privacy of subjects while having competitive recognition performance. However, due to improvements in skeleton recognition algorithms as well as motion and depth sensors, more details of motion characteristics can be preserved in the skeleton dataset, leading to potential privacy leakage. We first train classifiers to categorize private information from skeleton trajectories to investigate the potential privacy leakage from skeleton datasets. Our preliminary experiments show that the gender classifier achieves 87% accuracy on average, and the re-identification classifier achieves 80% accuracy on average with three baseline models: Shift-GCN, MS-G3D, and 2s-AGCN. We propose an anonymization framework based on adversarial learning to protect potential privacy leakage from the skeleton dataset. Experimental results show that an anonymized dataset can reduce the risk of privacy leakage while having marginal effects on action recognition performance even with simple anonymizer architectures. The code used in our experiments is available at https://github.com/ml-postech/Skeleton-anonymization/