| Title | Research on Data Classification of Intelligent Connected Vehicles Based on Scenarios |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Guo, Jiansheng, Qi, Liang, Suo, Jiao |
| Conference Name | 2021 International Conference on E-Commerce and E-Management (ICECEM) |
| Keywords | Big Data, Big Data Applications, compositionality, connected vehicles, data classification, Data security, Industries, Information security, Intelligent connected vehicle, intelligent data, Libraries, pubcrawl, resilience, Resiliency, Scalability, scenarios, security, Training |
| Abstract | The intelligent connected vehicle industry has entered a period of opportunity, industry data is accumulating rapidly, and the formulation of industry standards to regulate big data management and application is imminent. As the basis of data security, data classification has received unprecedented attention. By combing through the research and development status of data classification in various industries, this article combines industry characteristics and re-examines the framework of industry data classification from the aspects of information security and data assetization, and tries to find the balance point between data security and data value. The intelligent networked automobile industry provides support for big data applications, this article combines the characteristics of the connected vehicle industry, re-examines the data characteristics of the intelligent connected vehicle industry from the 2 aspects as information security and data assetization, and eventually proposes a scene-based hierarchical framework. The framework includes the complete classification process, model, and quantifiable parameters, which provides a solution and theoretical endorsement for the construction of a big data automatic classification system for the intelligent connected vehicle industry and safe data open applications. |
| DOI | 10.1109/ICECEM54757.2021.00038 |
| Citation Key | guo_research_2021 |
Research on Data Classification of Intelligent Connected Vehicles Based on Scenarios