只保留必要内容：面向隐私保护视频异常检测的帕累托最优数据最小化
Only Whats Necessary: Pareto Optimal Data Minimization for Privacy Preserving Video Anomaly Detection
AI总结：
本文提出Only What's Necessary框架，通过结合广度和深度的数据最小化机制，在保护隐私的同时保持异常检测相关线索，通过实验验证其在隐私与效用间的平衡效果。
摘要：
视频异常检测（VAD）系统在安全关键环境中日益普及，需要大量数据以实现准确检测。然而，此类数据可能包含个人身份信息（PII），如面部线索和敏感人口属性，这在欧盟通用数据保护条例（GDPR）下带来合规挑战。GDPR要求个人数据应严格限于特定处理目的。为此，我们引入Only What's Necessary，一种面向隐私设计的VAD框架，通过显式控制暴露给检测流水线的视觉信息量和类型。该框架结合基于广度和深度的数据最小化机制，以抑制PII同时保留异常检测相关线索。我们通过将最小化视频输入VAD模型和隐私推理模型，评估多种最小化配置。我们采用两种基于排名的方法以及帕累托分析，以表征隐私与效用之间的权衡。从非支配前沿中，我们识别出在最小化个人数据暴露的同时，检测性能仅有有限下降的操作点。在公开可用数据集上的广泛实验验证了所提框架的有效性。
英文摘要：
Video anomaly detection (VAD) systems are increasingly deployed in safety critical environments and require a large amount of data for accurate detection. However, such data may contain personally identifiable information (PII), including facial cues and sensitive demographic attributes, creating compliance challenges under the EU General Data Protection Regulation (GDPR). In particular, GDPR requires that personal data be limited to what is strictly necessary for a specified processing purpose. To address this, we introduce Only What's Necessary, a privacy-by-design framework for VAD that explicitly controls the amount and type of visual information exposed to the detection pipeline. The framework combines breadth based and depth based data minimization mechanisms to suppress PII while preserving cues relevant to anomaly detection. We evaluate a range of minimization configurations by feeding the minimized videos to both a VAD model and a privacy inference model. We employ two ranking based methods, along with Pareto analysis, to characterize the resulting trade off between privacy and utility. From the non-dominated frontier, we identify sweet spot operating points that minimize personal data exposure with limited degradation in detection performance. Extensive experiments on publicly available datasets demonstrate the effectiveness of the proposed framework.
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