Automatic, location-privacy preserving dashcam video sharing using blockchain and deep learning
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, das...
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| creator | Kim, Taehyoung Jung, Im Y. Hu, Yih-Chun |
| description | 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. |
| doi_str_mv | 10.1186/s13673-020-00244-8 |
| format | Article |
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Our experiments show that our proposed automatic video sharing system is cost-effective enough to be acceptable for deployment.</description><identifier>ISSN: 2192-1962</identifier><identifier>EISSN: 2192-1962</identifier><identifier>DOI: 10.1186/s13673-020-00244-8</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accident detection ; Artificial Intelligence ; Blockchain ; Cable television broadcasting industry ; Communications Engineering ; Computer Science ; Computer Systems Organization and Communication Networks ; Cryptography ; Deep learning ; Encryption ; Information Systems and Communication Service ; Information Systems Applications (incl.Internet) ; Machine learning ; Networks ; Privacy ; User Interfaces and Human Computer Interaction ; Video transmission</subject><ispartof>Human-centric Computing and Information Sciences, 2020-08, Vol.10 (1), Article 36</ispartof><rights>The Author(s) 2020</rights><rights>COPYRIGHT 2020 Springer</rights><rights>The Author(s) 2020. 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Cent. Comput. Inf. Sci</stitle><date>2020-08-26</date><risdate>2020</risdate><volume>10</volume><issue>1</issue><artnum>36</artnum><issn>2192-1962</issn><eissn>2192-1962</eissn><abstract>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.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1186/s13673-020-00244-8</doi><orcidid>https://orcid.org/0000-0002-9713-1757</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accident detection Artificial Intelligence Blockchain Cable television broadcasting industry Communications Engineering Computer Science Computer Systems Organization and Communication Networks Cryptography Deep learning Encryption Information Systems and Communication Service Information Systems Applications (incl.Internet) Machine learning Networks Privacy User Interfaces and Human Computer Interaction Video transmission |
| title | Automatic, location-privacy preserving dashcam video sharing using blockchain and deep learning |
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