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Automatic, location-privacy preserving dashcam video sharing using blockchain and deep learning
Human-centric Computing and Information Sciences ( IF 6.6 ) Pub Date : 2020-08-26 , DOI: 10.1186/s13673-020-00244-8
Taehyoung Kim , Im Y. Jung , Yih-Chun Hu

Today, many people use dashcams, and videos recorded on dashcams are often used as evidence of accident fault. People can upload videos of dashcam recordings with specific accident clips and share the videos with others who request them, by providing the time or location of an accident. However, dashcam videos are erased when the dashcam memory is full, so periodic backup is necessary for video sharing. It is inconvenient for dashcam owners to search for and transmit a requested video clip from backup videos. In addition, anonymity is not ensured, which may reduce location privacy by exposing the video owner’s location. To solve this problem, we propose a video sharing scheme with accident detection using deep learning coupled with automatic transfer to the cloud; we also propose ensuring data and operational integrity along with location privacy by using blockchain smart contracts. Furthermore, our proposed system uses proxy re-encryption to enhance the confidentiality of a shared video. Our experiments show that our proposed automatic video sharing system is cost-effective enough to be acceptable for deployment.

中文翻译:

使用区块链和深度学习自动保存位置隐私行车记录仪视频

如今,许多人使用行车记录仪,行车记录仪上录制的视频经常被用作事故故障的证据。人们可以通过提供事故发生的时间或地点,上传带有特定事故剪辑的行车记录仪录像的视频,并与提出要求的其他人共享录像。但是,当行车记录仪内存已满时,行车记录仪视频将被删除,因此,视频共享需要定期备份。对于行车记录仪所有者来说,从备份视频中搜索并传输所请求的视频剪辑很不方便。另外,无法确保匿名性,这可能会通过暴露视频所有者的位置来减少位置隐私。为了解决这个问题,我们提出了一种视频共享方案,该方案具有使用深度学习和自动传输到云的事故检测功能。我们还建议通过使用区块链智能合约确保数据和运营完整性以及位置隐私。此外,我们提出的系统使用代理重新加密来增强共享视频的机密性。我们的实验表明,我们提出的自动视频共享系统具有足够的成本效益,可以接受部署。
更新日期:2020-08-26
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