Name
Abstract | ||
|---|---|---|
ABSTRACTTime-series data gathered from smart spaces hide user's personal information that may arise privacy concerns. However, these data are needed to enable desired services. In this paper, we propose a privacy preserving framework based on Generative Adversarial Networks (GAN) that supports sensor-based applications while preserving the user identity. Experiments with two datasets show that the proposed model can reduce the inference of the user's identity while inferring the occupancy with a high level of accuracy. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3486611.3492234 | Embedded Network Sensor Systems |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tong Wu | 1 | 0 | 0.68 |
| Murtadha Aldeer | 2 | 0 | 2.70 |
| Tahiya Chowdhury | 3 | 0 | 2.03 |
| Amber Haynes | 4 | 0 | 0.34 |
| Fateme Nikseresht | 5 | 0 | 0.34 |
| Mahsa Pahlavikhah Varnosfaderani | 6 | 0 | 0.34 |
| Jiechao Gao | 7 | 17 | 3.89 |
| Arsalan Heydarian | 8 | 0 | 2.03 |
| Brad Campbell | 9 | 0 | 0.34 |
| Jorge Ortiz | 10 | 19 | 8.29 |