发表机构
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MultiGait是首个多传感器多视角多会话步态数据集及对应基准，研究发现部分隐私友好传感器仍有身份推理风险，现有方法跨会话泛化能力差。
AI 中文摘要
合适数据集的缺乏限制了针对新型智慧城市传感器（如热像仪、深度相机和激光雷达）隐私风险的研究。鉴于大量缺乏依据的隐私主张及其可能在人们日常生活中广泛部署，了解这些传感器在单独使用及同等条件对比下的隐私风险至关重要。借助MultiGait，我们收集了首个面向步态的多传感器、多视角、多会话数据集，用于对应及更深远的研究。该数据集经多个最先进的识别系统验证，包含199名个体的多种行走模式及标注个人属性，以确保其能为跨传感器识别、边缘端匿名化等高级研究提供支撑。MultiGait为严谨的隐私研究奠定了基础，这一点已通过涵盖8种传感器、4种视角和3次记录会话的大规模身份推理基准得到证实。我们的基准还意外发现，通常被认为对隐私友好的传感器仍存在相当大的身份推理风险，而现有方法较差的跨会话泛化能力则凸显了重要的研究空白。
英文摘要
A lack of suitable datasets has limited the research into the privacy risks of novel smart city sensors, such as thermal cameras, depth cameras, and lidar. Given the number of unsubstantiated privacy claims and their potential widespread deployment into many people's everyday life, understanding the privacy risks of these sensors -- in isolation and in like-for-like comparisons -- is crucial. With MultiGait, we collected the first multi-sensor, multi-perspective, multi-session gait-focused dataset, for the corresponding, and additional more far-reaching investigations. The dataset, validated with multiple state-of-the-art recognition systems, comprises various walking modes and annotated personal attributes for 199 individuals, to ensure the benefit for advanced studies including cross-sensor recognition and anonymization at the edge. MultiGait represents a foundation for rigorous privacy investigations, demonstrated through an extensive identity inference benchmark across eight sensors, four perspectives, and three recording sessions. Our benchmark incidentally reveals that sensors often assumed to be privacy-friendly do still entail considerable identity inference risks, while the poor cross-session generalization of existing methods underscores an important research gap.
