| Title | Data Provenance in Vehicle Data Chains |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Wilms, Daniel, Stoecker, Carsten, Caballero, Juan |
| Conference Name | 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) |
| Keywords | composability, Data models, Human Behavior, machine learning, Metrics, process control, Provenance, pubcrawl, Real-time Systems, Resiliency, security, Vehicular and wireless technologies, W3C |
| Abstract | With almost every new vehicle being connected, the importance of vehicle data is growing rapidly. Many mobility applications rely on the fusion of data coming from heterogeneous data sources, like vehicle and "smart-city" data or process data generated by systems out of their control. This external data determines much about the behaviour of the relying applications: it impacts the reliability, security and overall quality of the application's input data and ultimately of the application itself. Hence, knowledge about the provenance of that data is a critical component in any data-driven system. The secure traceability of the data handling along the entire processing chain, which passes through various distinct systems, is critical for the detection and avoidance of misuse and manipulation. In this paper, we introduce a mechanism for establishing secure data provenance in real time, demonstrating an exemplary use-case based on a machine learning model that detects dangerous driving situations. We show with our approach based on W3C decentralized identity standards that data provenance in closed data systems can be effectively achieved using technical standards designed for an open data approach. |
| DOI | 10.1109/VTC2021-Spring51267.2021.9448697 |
| Citation Key | wilms_data_2021 |
Data Provenance in Vehicle Data Chains