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.

翻译：城市尺度下跨分布式摄像头的人员再识别任务，必须应对视角变化、遮挡和域偏移带来的严重外观变化，同时遵守禁止共享原始图像的数据保护规定。我们提出CityGuard，一种用于去中心化监控中隐私保护身份检索的拓扑感知Transformer框架。该框架整合了三个核心组件：分散度自适应度量学习器根据特征分布调整实例级边界，以增强类内紧致性；空间条件注意力机制将粗略几何信息（如GPS或部署平面图）注入图基自注意力中，仅利用粗略几何先验即可实现投影一致的跨视角对齐，无需精密测量级标定；差分隐私嵌入映射与紧凑近似索引相结合，支持安全且成本高效的部署。这些设计共同产生了对视角变化、遮挡和域偏移具有鲁棒性的描述符，并在严格的差分隐私核算下实现了隐私性与实用性的可调平衡。在Market-1501及其他公开基准测试上的实验，辅以数据库级检索研究，均显示出相较于强基线在检索精度和查询吞吐量上的持续提升，证实了该框架在隐私敏感型城市身份匹配任务中的实用性。