ADGAN: Protect Your Location Privacy in Camera Data of Auto-Driving Vehicles
| Autor: | Qilong Han, Wei Li, Arwa Alrawais, Zhipeng Cai, Zuobin Xiong |
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| Rok vydání: | 2021 |
| Předmět: |
Information privacy
Visual perception Computer science 020208 electrical & electronic engineering Real-time computing 02 engineering and technology Inference attack Computer Science Applications Image (mathematics) Data modeling Visualization Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering State (computer science) Electrical and Electronic Engineering Downstream (networking) Information Systems |
| Zdroj: | IEEE Transactions on Industrial Informatics. 17:6200-6210 |
| ISSN: | 1941-0050 1551-3203 |
| Popis: | Computer vision and deep neural networks have been significantly promoting the development of visual perception in these years. Particularly, for autonomous vehicles, real-time image/video data is captured by onboard cameras and analyzed by computer vision techniques in many real applications. In the captured camera data, some contents can be used as auxiliary information to infer individuals’ locations and trajectories, which leads to severe privacy leakage but has been rarely studied. Thus, the goal of this article is to protect individuals’ location privacy by hiding side-channel information in the captured data while preserving the data utility for downstream applications. To this end, the technology of generative adversarial networks (GAN) is utilized to design two novel models, named ADGAN-I and ADGAN-II, both of which can take the original camera data as inputs and generate privacy-preserving outputs according to predefined sensitive object class. Thus, the processed camera data can defend location inference attack from adversaries in offline applications. Moreover, in ADGAN-I and ADGAN-II, the tradeoff between location privacy and data utility can be effectively balanced. Finally, the results of extensive real-data experiments validate the superiority of our proposed models over the state of the arts in utility preservation and privacy protection for autonomous vehicles’ images and videos. |
| Databáze: | OpenAIRE |
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