The smart building privacy challenge

ABSTRACTTime-series data gathered from smart spaces hide user's personal information that may arise privacy concerns. However, these data are needed to enable desired services. In this paper, we propose a privacy preserving framework based on Generative Adversarial Networks (GAN) that supports sensor-based applications while preserving the user identity. Experiments with two datasets show that the proposed model can reduce the inference of the user's identity while inferring the occupancy with a high level of accuracy.