Interpretable Machine Learning for Privacy-Preserving IoT and Pervasive Systems
Benjamin Baron, Mirco Musolesi · arXiv (Cornell University) · 2017
Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated traces violate their privacy.