A Blackbox Access to Load Forecasting Models Is All You Need to Breach Privacy

Machine learning and deep learning models play a crucial role in optimizing smart grid operations. While offering valuable utility, concerns arise regarding their exposure of sensitive information. Previous studies have focused on optimizing load forecasting models, such as Long Short Term Memory (LSTM), overlooking their associated risks of revealing private information about the consumer. Existing privacy preservation studies have focused mainly on addressing risks associated with classification models, but similar concerns may exist in their load forecasting counterparts. In this paper, we investigate potential data leakage from deep learning-based load forecasting models. Our study analyzes the ability of personalized forecasting models to leak global properties about the consumer. We present a novel property inference attack tailored for forecasting models that can be executed in a black box setting, with only query access to the target model. We demonstrate that black box access to an LSTM model can reveal sensitive information about the user, with the attack achieving results comparable to having direct access to the data (with the difference being as low as 1% in Area Under the ROC Curve). This highlights the necessity of securing access to forecasting models with the same level of diligence as the data itself.