Baron, B;
Musolesi, M;
(2020)
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems.
IEEE Pervasive Computing
, 19
(1)
pp. 73-82.
10.1109/MPRV.2019.2918540.
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1710.08464v6.pdf - Accepted Version Available under License : See the attached licence file. Download (3MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Interpretable Machine Learning for Privacy-Preserving Pervasive Systems |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/MPRV.2019.2918540 |
| Publisher version: | https://doi.org/10.1109/MPRV.2019.2918540 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Privacy, Machine learning, Task analysis, Data privacy, Computational modeling, Feature extraction |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10091113 |
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