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Interpretable Machine Learning for Privacy-Preserving Pervasive Systems
IEEE Pervasive Computing ( IF 1.6 ) Pub Date : 2020-01-01 , DOI: 10.1109/mprv.2019.2918540
Benjamin Baron 1 , Mirco Musolesi 1
Affiliation  

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.

中文翻译:

隐私保护普及系统的可解释机器学习

我们与无处不在的系统的日常交互会生成追踪人类行为各个方面的痕迹,并使机器学习算法能够提取有关用户的潜在信息。在本文中,我们提出了一个机器学习可解释性框架,使用户能够了解这些生成的痕迹如何侵犯他们的隐私。
更新日期:2020-01-01
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