This paper investigates the potential privacy risks associated with forecasting models, with specific emphasis on their application in the context of smart grids. While machine learning and deep learning algorithms offer valuable utility, concerns arise regarding their exposure of sensitive information. Previous studies have focused on classification models, overlooking risks associated with forecasting models. Deep learning based forecasting models, such as Long Short Term Memory (LSTM), play a crucial role in several applications including optimizing smart grid systems but also introduce privacy risks. Our study analyzes the ability of forecasting models to leak global properties and privacy threats in smart grid systems. We demonstrate that a black box access to an LSTM model can reveal a significant amount of information equivalent to having access to the data itself (with the difference being as low as 1% in Area Under the ROC Curve). This highlights the importance of protecting forecasting models at the same level as the data.

翻译：本文研究了预测模型潜在隐私风险，并特别聚焦其在智能电网中的应用场景。虽然机器学习和深度学习算法具有重要实用价值，但其暴露敏感信息的问题引发担忧。现有研究主要关注分类模型，忽视了预测模型的风险。基于深度学习的预测模型（如长短期记忆网络LSTM）在包括智能电网系统优化在内的多种应用中发挥关键作用，但同时也引入了隐私威胁。本研究分析了预测模型泄露全局属性及智能电网系统隐私威胁的能力。我们证明，仅通过黑盒访问LSTM模型即可获取相当于直接访问原始数据的信息量（ROC曲线下面积差异低至1%）。这凸显了将预测模型与数据置于同等保护级别的重要性。