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[2602.18047] CityGuard: Graph-Aware Private Descriptors for Bias-Resilient Identity Search Across Urban Cameras
Summary
CityGuard introduces a novel framework for privacy-preserving identity retrieval across urban surveillance cameras, addressing challenges like viewpoint variation and occlusion.
Why It Matters
As urban surveillance becomes more prevalent, ensuring privacy while maintaining effective identity search is crucial. CityGuard's innovative approach balances the need for security and utility, making it relevant for policymakers, technologists, and privacy advocates.
Key Takeaways
- CityGuard employs a topology-aware transformer for decentralized identity retrieval.
- The framework enhances privacy through differentially private embedding maps.
- Spatially conditioned attention improves cross-view alignment without extensive calibration.
- Results show significant improvements in retrieval precision and query throughput.
- The approach addresses critical privacy concerns in urban surveillance systems.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.18047 (cs) [Submitted on 20 Feb 2026] Title:CityGuard: Graph-Aware Private Descriptors for Bias-Resilient Identity Search Across Urban Cameras Authors:Rong Fu, Wenxin Zhang, Yibo Meng, Jia Yee Tan, Jiaxuan Lu, Rui Lu, Jiekai Wu, Zhaolu Kang, Simon Fong View a PDF of the paper titled CityGuard: Graph-Aware Private Descriptors for Bias-Resilient Identity Search Across Urban Cameras, by Rong Fu and 8 other authors View PDF HTML (experimental) Abstract:City-scale person re-identification across distributed cameras must handle severe appearance changes from viewpoint, occlusion, and domain shift while complying with data protection rules that prevent sharing raw imagery. We introduce CityGuard, a topology-aware transformer for privacy-preserving identity retrieval in decentralized surveillance. The framework integrates three components. A dispersion-adaptive metric learner adjusts instance-level margins according to feature spread, increasing intra-class compactness. Spatially conditioned attention injects coarse geometry, such as GPS or deployment floor plans, into graph-based self-attention to enable projectively consistent cross-view alignment using only coarse geometric priors without requiring survey-grade calibration. Differentially private embedding maps are coupled with compact approximate indexes to support secure and cost-efficient deployment. Together these designs produce descriptors robust to vie...