Interpretable Machine Learning for Privacy-Preserving IoT and Pervasive Systems.

The presence of pervasive computing in our everyday lives and emergence of the Internet of Things, such as the interaction of users with connected devices like smartphones or home appliances generate increasing amounts of traces that reflect users' behavior. A plethora of machine learning techniques enable service providers to process these traces to extract latent information about the users. While most of the existing projects have focused on the accuracy of these techniques, little work has been done on the interpretation of the inference and identification algorithms based on them. In this paper, we propose a machine learning interpretability framework for inference algorithms based on data collected through IoT and pervasive systems and we outline the open challenges in this research area. Our interpretability framework enable users to understand how the traces they generate could expose their privacy, while allowing for usable and personalized services at the same time.