Name
Abstract | ||
|---|---|---|
Since trajectory data is widely collected and utilized for scientific research and business purpose, publishing trajectory without proper privacy-policy leads to an acute threat to individual data. Recently, several methods, i.e., k-anonymity, l-diversity, t-closeness have been studied, though they tend to protect by reducing data depends on a feature of each method. When a strong privacy protection is required, these methods have excessively reduced data utility that may affect the result of scientific research. In this research, we suggest a novel approach to tackle this existing dilemma via an adding noise trajectory on a vector-based grid environment. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BigComp48618.2020.00-34 | 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) |
Keywords | DocType | ISSN |
Keywords-—-Noise-trajectory,-Privacy-Publishing-Data,-Surrogate-Vector | Conference | 2375-933X |
ISBN | Citations | PageRank |
978-1-7281-6035-1 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rashid Tojiboev | 1 | 0 | 0.34 |
| Wookey Lee | 2 | 196 | 29.22 |
| Charles Cheolgi Lee | 3 | 17 | 1.72 |