CityGuard: Graph-Aware Private Descriptors for Bias-Resilient Identity Search Across Urban Cameras

• URL: http://arxiv.org/abs/2602.18047v1
• Date: Fri, 20 Feb 2026 08:00:17 GMT
• 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,
• Abstract summary: CityGuard is a topology-aware transformer for privacy-preserving identity retrieval in decentralized surveillance.<n>A dispersion-adaptive metric learner adjusts instance-level margins according to feature spread, increasing intra-class compactness.<n>Private embedding maps are coupled with compact approximate indexes to support secure and cost-efficient deployment.
• Score: 16.147944008359957
• License: http://creativecommons.org/licenses/by/4.0/
• 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 viewpoint variation, occlusion, and domain shifts, and they enable a tunable balance between privacy and utility under rigorous differential-privacy accounting. Experiments on Market-1501 and additional public benchmarks, complemented by database-scale retrieval studies, show consistent gains in retrieval precision and query throughput over strong baselines, confirming the practicality of the framework for privacy-critical urban identity matching.

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