UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Interpretable Machine Learning for Privacy-Preserving Pervasive Systems

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. Green open access

[img]
Preview
Text
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 > 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
Downloads since deposit
0Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item