# Study on DID Application Methods for Blockchain-Based Traffic Forensic Data
## 3 Forensic of Digital Data Collected from the Accident Vehicle
### Digital Forensic Framework of Vehicle Sensor Analysis Based on Blockchain  
Various kinds of sensor data from a vehicle accident collected for traffic accident investigation must be provided to multiple verification authorities without being hacked or altered for evidence analysis use. The vehicle accident sensing data to be provided to multiple specialized agencies can use DID applied traffic accident digital forensic framework for managing a blockchain-based self-sovereignty identity. The transparency and integrity of the video records and driving records stored at the time of the accident is guaranteed by a blockchain-based platform, and this also remains as important evidence for determining the cause of a traffic accident following an investigation through a traffic accident analysis digital forensics platform and a procedural analysis process. The blockchain generally utilizes an intelligent contract to automate contract terms, a hashing algorithm to safeguard information confidentiality, a consensus mechanism to safeguard data integrity, and an asymmetric key to safeguard data flow security [19]. The DID(Decentralized Identity) applied for managing self-sovereignty identity manages the contents of personal information and various kinds of verification directly or verifies and manages them by sharing each issuing agency's public key based on trust as seen in Figure 3. The components for managing the self-sovereignty identity if vehicle \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Details** \\ \hline DSRC msgID & Unique identifier which signifies upload of collected information (PVD: 10) \\ \hline DDate Time & GPS time information (in accordance with SAE \\  & J2735 standards) \\ \hline Longitude & Longitude location information (in accordance with SAE \\  & J2735 standards) \\ \hline Latitude & Latitude location information (in accordance with SAE \\  & J2735 standards) \\ \hline OBE ID & Unique identifier assigned to OBE \\ \hline Manufacturer/Model & OBE manufacturer and model name \\ \hline Firmware version & Firmware version information \\ \hline Firmware Upgrade Status & Firmware download or upgrade status \\ \hline Map version & Map version \\ \hline RSSI & Strength of signal reception \\ \hline Traffic Collection Information & Traffic information collected by OBE \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Probe Vehicle Data. accident sensor data consist of DID and DID documents used as an identifier and a way of verification, VC (verifiable Credential) used as an ID for storage, and VP(Verifiable Presentation), which is used as an ID for submission. The main participants of an investigation of accident vehicle sensor data are the Issuer, which issues the VC of the major participants, the Holder, which processes the VC it receives into VP and submits it to the verifying agency, the Verifier, which receives the VP from the Holder and verifies its authenticity, and the Identifier Registry, which is a distributed repository that stores DID and ID-related information. VC is authentication of vehicle identification such as the vehicle registration card that was issued to the vehicle user by the issuing agency and the vehicle identification number. However, the user does not use the VC directly and processes it into VP which is ID for submission, and applies vehicle accident data DID. From the properties of the resident registration card VC of the vehicle owner and the vehicle registration card VC, for example, the name and age are extracted from the resident registration card VC while the vehicle identification number is extracted from the vehicle registration card, which are then created into a single VP and consequently submitted to the agency investigating the accident. The DID in this case is used as the identifier that goes in the VC and the VP, so it is used as the verifying identifier for securing the integrity of the accident vehicle sensing data [20].