Abstract
Day to day interactions with wearable and pervasive systems lead to collected data that capture various aspects of human behavior and enable machine learning algorithms to extract extensive information about users. We discuss privacy risk awareness, and ways to preserve privacy and integrate it in current frameworks.
| Original language | English |
|---|---|
| Article number | 9154758 |
| Pages (from-to) | 60-66 |
| Number of pages | 7 |
| Journal | IEEE Pervasive Computing |
| Volume | 19 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published (in print/issue) - 3 Aug 2020 |
Bibliographical note
Funding Information:This work was supported by the European Union Horizon 2020 MSCA ITN ACROSSING Project (GA 676157).
Publisher Copyright:
© 2002-2012 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
Funding
Funding Information: This work was supported by the European Union Horizon 2020 MSCA ITN ACROSSING Project (GA 676157). Publisher Copyright: © 2002-2012 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Fingerprint
Dive into the research topics of 'Privacy Risk Awareness in Wearables and the Internet of Things'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver