时间粒度对家庭负荷曲线社会人口推断的影响
The Impact of Temporal Granularity on Socio-Demographic Inference from Household Load Profiles
AI总结：
本文通过分析15分钟到7天不同粒度负荷曲线对8个社会人口属性的预测影响，揭示了隐私-效用权衡中时间分辨率、特征提取和分类器选择的联合作用。
摘要：
智能电表数据可以揭示家庭敏感的社会人口特征，引发隐私担忧。虽然这一风险已在固定粒度下得到证实，但时间分辨率在塑造推断性能中的作用尚未得到充分探索。本文通过分析从15分钟到7天不同粒度的负荷曲线如何影响1589户家庭一年数据中八个社会人口属性的可预测性，填补了这一空白。我们引入了一个评估框架，其中分类器在全年数据上训练，但在任意周上测试，迫使模型跨季节和每周变化进行泛化。我们的结果显示了三个主要发现。首先，虽然粗化粒度降低了预测准确性，但出现了两个平台期：性能在15分钟到1小时之间稳定，以及在1到7天之间再次稳定。这揭示了在不牺牲效用的情况下进行数据最小化的机会。其次，可解释的手工特征和tsfresh特征仍然与基于CNN的自编码器嵌入具有竞争力，而XGBoost始终优于其他分类器。第三，特征重要性分析突出了静态和动态属性之间的差异：即使从粗粒度数据中也能推断出住宅面积，而游泳池使用则需要细粒度的时间信号。总体而言，我们的研究为智能计量中的隐私-效用权衡提供了新的见解，显示了时间分辨率、特征提取和分类器选择如何共同影响社会人口推断。
英文摘要：
Smart meter data can reveal sensitive socio-demographic characteristics of households, raising privacy concerns. While this risk has been demonstrated at fixed granularities, the role of temporal resolution in shaping inference performance remains insufficiently explored. This paper addresses this gap by analyzing how load profiles with granularities from 15 minutes to 7 days affect the predictability of eight socio-demographic attributes in a dataset of 1,589 households over one year. We introduce an evaluation framework where classifiers are trained on year-round data but tested on arbitrary weeks, forcing generalization across seasonal and weekly variations. Our results show three main findings. First, while coarsening granularity reduces predictive accuracy, two plateaus emerge: performance is stable between 15 minutes and 1 hour, and again between 1 and 7 days. This reveals opportunities for data minimization without sacrificing utility. Second, interpretable handcrafted and tsfresh features remain competitive with CNN-based autoencoder embeddings, while XGBoost consistently outperforms alternative classifiers. Third, feature importance analysis highlights differences between static and dynamic attributes: dwelling size can be inferred even from coarse data, whereas swimming pool usage requires fine-grained temporal signals. Overall, our study provides new insights into the privacy-utility trade-off in smart metering, showing how temporal resolution, feature extraction, and classifier choice jointly influence socio-demographic inference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
