# A Real-Time Privacy-Preserving Behavior Recognition System via Edge-Cloud Collaboration

arXiv: https://arxiv.org/abs/2601.22938 · HTML: https://mlsanity.com/paper/2601.22938/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [AI Flow at the Network Edge](https://mlsanity.com/paper/2411.12469/) (2024)
- [AI Flow: Perspectives, Scenarios, and Approaches](https://mlsanity.com/paper/2506.12479/) (2025)
- [Task-Oriented Feature Compression for Multimodal Understanding via Device-Edge Co-Inference](https://mlsanity.com/paper/2503.12926/) (2025)
- [Integrating Reinforcement Learning with Visual Generative Models: Foundations and Advances](https://mlsanity.com/paper/2508.10316/) (2025)
- [VIP: Visual Information Protection through Adversarial Attacks on Vision-Language Models](https://mlsanity.com/paper/2507.08982/) (2025)
- [Theoretical Foundations of Scaling Law in Familial Models](https://mlsanity.com/paper/2512.23407/) (2025)
- [Single-Pixel Vision-Language Model for Intrinsic Privacy-Preserving Behavioral Intelligence](https://mlsanity.com/paper/2601.17050/) (2026)

## Built on by (most cited first, 1 of 1)

- [Privacy-Aware Camera 2.0 Technical Report](https://mlsanity.com/paper/2603.04775/) (2026)

## Abstract (arXiv)

As intelligent sensing expands into high-privacy environments such as restrooms and changing rooms, the field faces a critical privacy-security paradox. Traditional RGB surveillance raises significant concerns regarding visual recording and storage, while existing privacy-preserving methods-ranging from physical desensitization to traditional cryptographic or obfuscation techniques-often compromise semantic understanding capabilities or fail to guarantee mathematical irreversibility against reconstruction attacks. To address these challenges, this study presents a novel privacy-preserving perception technology based on the AI Flow theoretical framework and an edge-cloud collaborative architecture. The proposed methodology integrates source desensitization with irreversible feature mapping. Leveraging Information Bottleneck theory, the edge device performs millisecond-level processing to transform raw imagery into abstract feature vectors via non-linear mapping and stochastic noise injection. This process constructs a unidirectional information flow that strips identity-sensitive attributes, rendering the reconstruction of original images impossible. Subsequently, the cloud platform utilizes multimodal family models to perform joint inference solely on these abstract vectors to detect abnormal behaviors. This approach fundamentally severs the path to privacy leakage at the architectural level, achieving a breakthrough from video surveillance to de-identified behavior perception and offering a robust solution for risk management in high-sensitivity public spaces.