Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

Bi, Renwan; Zhao, Mingfeng; Ying, Zuobin; Tian, Youliang; Xiong, Jinbo

doi:10.1016/j.dcan.2022.07.013

Cited by 7 publications

(1 citation statement)

References 29 publications

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“…Nevertheless, the captured images contain a large amount of private information. Uploading the raw data directly to edge servers without any additional security measures will lead to serious privacy disclosure problems and even threaten social public security [12]. For example, continuous image frames record the street, buildings and other information that the vehicles pass through.…”

Section: Introductionmentioning

confidence: 99%

Achieving Lightweight and Privacy-Preserving Object Detection for Connected Autonomous Vehicles

Xiong

Tian

et al. 2023

IEEE Internet Things J.

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Connected autonomous vehicles (CAVs) are capable of capturing high-definition images from onboard sensors, which can be used to facilitate the detection of objects in the vicinity. Such images may, however, contain sensitive information (e.g., human faces and license plates) as well as the indirect location of CAVs. To protect the object privacy of images shared by CAVs, this paper proposes a privacy-preserving object detection (P2OD) framework. Specifically, we propose multiple secure computing protocols designed to construct a privacy-preserving Faster R-CNN model to securely extract features and boundingboxes of objects in an image. By leveraging edge computing (with higher performance computation and lower latency, in comparison to cloud-based solutions), CAVs randomly split the captured images and upload them to two non-collusive edge servers. Both servers will then perform the P2OD framework cooperatively to directly detect objects over random image shares without exposing sensitive information. The theoretical analysis demonstrates the security, correctness and efficiency of the P2OD framework, and the experimental findings show that the P2OD framework can effectively protect the classification and location privacy of image objects for CAVs. Compared with the original Faster R-CNN model, the classification and regression errors of the P2OD framework can be controlled within 10 −12 and 10 −14 , respectively.

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Section: Introductionmentioning

confidence: 99%