Spears and shields: attacking and defending deep model co-inference in vehicular crowdsensing networks
Abstract Vehicular CrowdSensing (VCS) network is one of the key scenarios for future 6G ubiquitous artificial intelligence. In a VCS network, vehicles are recruited for collecting urban data and performing deep model inference. Due to the limited computing power of vehicles, we deploy a device-edge...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
SpringerOpen
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/9938b451c6c24ecdb58ab6edc2ac9bad |
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| Sumario: | Abstract Vehicular CrowdSensing (VCS) network is one of the key scenarios for future 6G ubiquitous artificial intelligence. In a VCS network, vehicles are recruited for collecting urban data and performing deep model inference. Due to the limited computing power of vehicles, we deploy a device-edge co-inference paradigm to improve the inference efficiency in the VCS network. Specifically, the vehicular device and the edge server keep a part of the deep model separately, but work together to perform the inference through sharing intermediate results. Although vehicles keep the raw data locally, privacy issues still exist once attackers obtain the shared intermediate results and recover the raw data in some way. In this paper, we validate the possibility by conducting a systematic study on the privacy attack and defense in the co-inference of VCS network. The main contributions are threefold: (1) We take the road sign classification task as an example to demonstrate how an attacker reconstructs the raw data without any knowledge of deep models. (2) We propose a model-perturbation defense to defend against such attacks by injecting some random Laplace noise into the deep model. A theoretical analysis is given to show that the proposed defense mechanism achieves $$\epsilon$$ ϵ -differential privacy. (3) We further propose a Stackelberg game-based incentive mechanism to attract the vehicles to participate in the co-inference by compensating their privacy loss in a satisfactory way. The simulation results show that our proposed defense mechanism can significantly reduce the effects of the attacks and the proposed incentive mechanism is very effective. |
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