A Blackbox Model Is All You Need to Breach Privacy: Smart Grid
  Forecasting Models as a Use Case
Hussein Aly Abdulaziz Al-Ali Abdullah Al-Ali Qutaibah Malluhi
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.