Interpretable Machine Learning for Privacy-Preserving Pervasive Systems

Autor:	Mirco Musolesi, Benjamin Baron
Přispěvatelé:	Benjamin Baron, Mirco Musolesi
Rok vydání:	2017
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Information privacy Computer Science - Cryptography and Security Ubiquitous computing Interpretable machine learning privacy location-based social networks business.industry Computer science Feature extraction Machine Learning (stat.ML) Machine learning computer.software_genre Computer Science Applications Machine Learning (cs.LG) Privacy preserving Computational Theory and Mathematics Statistics - Machine Learning Pervasive systems Task analysis Artificial intelligence business computer Cryptography and Security (cs.CR) Software Interpretability
DOI:	10.48550/arxiv.1710.08464
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c13666195dc5838c8e4dc41ca0f7770f Zobrazit plný text záznamu