Name
Abstract | ||
|---|---|---|
Collecting large amounts of data from mobile devices has become pervasive as a basis for data-driven decision making, but this collection dramatically increases the surface area for undesirable actions against user’s privacy, e.g., leakage of user information. Local Differential Privacy (LDP) is a high-level architecture for privacy preservation based on probability and statistical inference theor... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/PerComWorkshops53856.2022.9767402 | 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) |
Keywords | DocType | ISBN |
data privacy,mobile device,adaptive estimation,local differential privacy performance | Conference | 978-1-6654-1647-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lucas Gallindo | 1 | 0 | 0.34 |
| Bruno Filizola Leal | 2 | 0 | 0.34 |
| Luiz Miguel Cerqueira | 3 | 0 | 0.34 |
| Anderson Morais | 4 | 0 | 0.34 |
| Soo-Hyung Kim | 5 | 191 | 49.03 |