Research map / Video / Action recognition
Privacy-Preserving Video Classification with Convolutional Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace Softmax with the approximate Softmax function | ConvNet video classifier · RAVDESS test set | video classification accuracy: 56% -> 56.8% | Helped |
| Use passive 3PC rather than passive 2PC | Private video classification · RAVDESS videos | Avg. Time (sec): 511.64 -> 13.12 | Helped |
| Use active 3PC rather than active 2PC | Private video classification · RAVDESS videos | Avg. Time (sec): 8423.81 -> 48.20 | Helped |
| Use active 4PC rather than active 3PC | Private video classification · RAVDESS videos | Avg. Time (sec): 48.20 -> 18.40 | Helped |
| Use active rather than passive security in 3PC | Private video classification · RAVDESS videos | Avg. Time (sec): 13.12 -> 48.20 | Hurt |
Closest papers by meaning
- Privacy Beyond Pixels: Latent Anonymization for Privacy-Preserving Video Understanding (ICLR 2026 · similarity 0.88)
- ViP: A Differentially Private Foundation Model for Computer Vision (ICML 2024 · similarity 0.84)
- Selective Interpretable and Motion Consistent Privacy Attribute Obfuscation for Action Recognition (CVPR 2024 · similarity 0.84)
- Certification for Differentially Private Prediction in Gradient-Based Training (ICML 2025 · similarity 0.83)
- Balancing Privacy and Performance: A Many-in-One Approach for Image Anonymization (AAAI 2025 · similarity 0.83)
- Validating Privacy-Preserving Face Recognition under a Minimum Assumption (CVPR 2024 · similarity 0.82)
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