Name
Abstract | ||
|---|---|---|
Mobility data mining can improve decision making, from planning transports in metropolitan areas to localizing services in towns. However, unrestricted access to such data may reveal sensible locations and pose safety risks if the data is associated to a specific moving individual. This is one of the many reasons to consider trajectory anonymization. |
Year | Venue | Field |
|---|---|---|
2018 | PSD | Data mining,Swap (computer programming),Computer science,Metropolitan area,Trajectory |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
25 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julián Salas | 1 | 0 | 1.35 |
| David Megías | 2 | 205 | 24.13 |
| Vicenç Torra | 3 | 2666 | 234.27 |