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.
Benjamin Baron, Mirco Musolesi (2020). Interpretable Machine Learning for Privacy-Preserving Pervasive Systems. IEEE PERVASIVE COMPUTING, 19(1), 73-82 [10.1109/MPRV.2019.2918540].
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems
Mirco Musolesi
2020
Abstract
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.File in questo prodotto:
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