Existing research on privacy-preserving Human Activity Recognition (HAR) typically evaluates methods against a binary paradigm: clear video versus a single privacy transformation. This limits cross-method comparability and obscures the nuanced relationship between privacy strength and recognition utility. We introduce \textit{PrivHAR-Bench}, a multi-tier benchmark dataset designed to standardize the evaluation of the \textit{Privacy-Utility Trade-off} in video-based action recognition. PrivHAR-Bench applies a graduated spectrum of visual privacy transformations: from lightweight spatial obfuscation to cryptographic block permutation, to a curated subset of 15 activity classes selected for human articulation diversity. Each of the 1,932 source videos is distributed across 9 parallel tiers of increasing privacy strength, with additional background-removed variants to isolate the contribution of human motion features from contextual scene bias. We provide lossless frame sequences, per-frame bounding boxes, estimated pose keypoints with joint-level confidence scores, standardized group-based train/test splits, and an evaluation toolkit computing recognition accuracy and privacy metrics. Empirical validation using R3D-18 demonstrates a measurable and interpretable degradation curve across tiers, with within-tier accuracy declining from 88.8\% (clear) to 53.5\% (encrypted, background-removed) and cross-domain accuracy collapsing to 4.8\%, establishing PrivHAR-Bench as a controlled benchmark for comparing privacy-preserving HAR methods under standardized conditions. The dataset, generation pipeline, and evaluation code are publicly available.

翻译：现有关于隐私保护人类活动识别（HAR）的研究通常基于二元范式评估方法：即清晰视频与单一隐私变换的对比。这限制了跨方法可比性，并掩盖了隐私强度与识别效用之间的细微关系。我们提出\textit{PrivHAR-Bench}，一个多层级基准数据集，旨在标准化视频动作识别中\textit{隐私-效用权衡}的评估。PrivHAR-Bench对精心挑选的15个涵盖人体关节多样性动作类别子集，应用了从轻量级空间模糊到密码学块置换的渐进式视觉隐私变换谱。全部1,932个源视频分布在9个隐私强度递增的并行层级中，并附加背景去除变体，以隔离人体运动特征与上下文场景偏差的贡献。我们提供无损帧序列、逐帧边界框、带有联合置信度分数的估计姿态关键点、标准化分组训练/测试集，以及用于计算识别准确率和隐私度量的评估工具包。基于R3D-18的实证验证显示，各层级间存在可测量且可解释的性能衰减曲线：层内准确率从88.8%（清晰）下降至53.5%（加密且背景去除），跨域准确率骤降至4.8%，从而确立PrivHAR-Bench作为在标准化条件下比较隐私保护HAR方法的受控基准。数据集、生成流程及评估代码均已公开。