| Title | Privacy Modelling in Contact Tracing |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Øye, Marius Mølnvik, Yang, Bian |
| Conference Name | 2021 International Conference on Computational Science and Computational Intelligence (CSCI) |
| Keywords | Computational modeling, Consumer electronics, COVID-19, data privacy, Deceases, Electric potential, expert systems, Human Behavior, human factors, Pandemics, privacy, Protocols, pubcrawl, Scalability, Scientific computing, security |
| Abstract | Contact tracing is a particularly important part of health care and is often overlooked or forgotten up until right when it is needed the most. With the wave of technological achievements in the last decade, a digital perspective for aid in contact tracing was a natural development from traditional contact tracing. When COVID-19 was categorized as a pandemic, the need for modernized contact tracing solutions became apparent, and highly sought after. Solutions using the Bluetooth protocol and/or Global Positioning System data (GPS) were hastily made available to the public in nations all over the world. These solutions quickly became criticized by privacy experts as being potential tools for tracking. |
| DOI | 10.1109/CSCI54926.2021.00260 |
| Citation Key | oye_privacy_2021 |
Privacy Modelling in Contact Tracing