The nationwide rollout of smart meters in private households raises privacy concerns: Is it possible to extract privacy-sensitive information from a household’s power consumption? For a small sample of 869 Upper Austrian households, information about consumption-heavy amenities and household characteristics are available. This work studies the detection of households with swimming pools (the most common amenity in the dataset) using Convolutional Neural Networks (CNNs) applied on load heatmaps constructed from load profiles. Although only a small dataset is available, results show that by using CNNs, privacy can be broken automatically, i.e., without the time-consuming, manual feature generation. The method even slightly outperforms a previous approach that relies on a nearest neighbor classifier with engineered features.

中文翻译：

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利用卷积神经网络从智能计量数据中进行池检测

全国范围内在私人家庭中推出智能电表引起了人们对隐私的担忧：是否有可能从家庭的用电量中提取对隐私敏感的信息？对于869个上奥地利州家庭的一小部分样本，可以获得有关大量消费的便利设施和家庭特征的信息。这项工作研究了卷积神经网络（CNN）在有负荷分布图构成的负荷热图上的应用，从而发现了有游泳池的家庭（数据集中最常见的便利设施）。尽管只有很少的数据集，但结果表明，使用CNN可以自动破坏隐私，即无需费时的手动特征生成。该方法甚至略胜于以前的方法，后者依赖于具有工程特征的最近邻居分类器。