Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/71970| Título: | Decentralized privacy-preserving proximity tracing |
| Autor(es): | Troncoso, Carmela Pereira, José Oliveira, Rui Barbosa, Manuel Payer, Mathias Hubaux, Jean-Pierre Salathe, Marcel Larus, James Lueks, Wouter Stadler, Theresa |
| Data: | 2020 |
| Editora: | IEEE |
| Revista: | Bulletin of the IEEE Computer Society Technical Committee on Data Engineering |
| Resumo(s): | [Excerpt] This document describes and analyzes a system for secure and privacy-preserving proximity tracing at large scale. This system provides a technological foundation to help slow the spread of SARS-CoV-2 by simplifying and accelerating the process of notifying people who might have been exposed to the virus so that they can take appropriate measures to break its transmission chain. The system aims to minimise privacy and security risks for individuals and communities and guarantee the highest level of data protection. [...] |
| Tipo: | Artigo |
| URI: | https://hdl.handle.net/1822/71970 |
| Arbitragem científica: | yes |
| Acesso: | Acesso restrito UMinho |
| Aparece nas coleções: |
Ficheiros deste registo:
| Ficheiro | Descrição | Tamanho | Formato | |
|---|---|---|---|---|
| p36.pdf Acesso restrito! | 1,73 MB | Adobe PDF | Ver/Abrir |
