| Autor: |
Murtadha Aldeer, Arsalan Heydarian, Brad Campbell, Jiechao Gao, Tahiya Chowdhury, Amber Haynes, Jorge Ortiz, Fateme Nikseresht, Mahsa Pahlavikhah Varnosfaderani, Tong Wu |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
BuildSys@SenSys |
| Popis: |
Time-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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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