MLOct 23, 2017
Interpretable Machine Learning for Privacy-Preserving Pervasive SystemsBenjamin Baron, Mirco Musolesi
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.