# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 6 Discussion  
### Impact of Character Length on the Uniqueness and Robustness of Unique Identification  
In Section 5, a unique identifier length of 1500 is selected. However, different lengths of unique identifiers demonstrate varying degrees of uniqueness and robustness. This section delves into the impact of character length on the uniqueness and robustness of unique identifiers to ascertain the appropriate range for constructing such identifiers. Initially, uniqueness experiments are conducted, wherein unique identifiers of different lengths are generated for six datasets, encompassing character lengths of 500, 1000, 1500, 2000, 2500, and 3000. Subsequently, the bit error rate (BER) between unique identifiers of varying lengths for each dataset is computed to assess the uniqueness. The experimental findings, illustrated in Figure 12, reveal that as the length of unique identifiers increases, the BER values between pairs of identifiers gradually rise. This indicates that the identifiers become increasingly dissimilar, reflecting stronger uniqueness and distinctiveness, which are essential for ensuring reliable copyright protection and differentiation. Similarly, robustness experiments are conducted. This involves unique identifiers of various character lengths, ranging from 500 to 3000 characters, across the same datasets. Various degrees of attacks, such as cropping, feature deletion, and layer deletion, are applied to these identifiers. The BER values between the constructed unique identifiers from the disrupted data and the original unique identifiers are then calculated to evaluate robustness. As depicted in Figure 13, the results indicate that as the character length of unique identifiers increases, the BER values also escalate, implying diminished robustness. Analysis derived from the uniqueness and robustness experiments suggests that overly short character sequences may result in identical sequences across different map areas, thus compromising uniqueness. This occurs because identical feature sequences might manifest in diverse map areas, posing challenges in accurately distinguishing between geographical locations. Additionally, short sequences may fail to encapsulate the intricate structures and features of vector maps adequately, potentially leading to critical information loss during unique identifier generation. Conversely, longer sequences are more susceptible to noise, deformation, and environmental changes, thereby reducing robustness. Prolonged sequences may excessively react to minor variations, deeming slight differences in geographical locations as significant. Therefore, to strike a balance between uniqueness and robustness, it is imperative to select a moderately sized character sequence length. This ensures the precise identification of distinct map areas while maintaining the requisite level of robustness against noise and variations. Based on a comprehensive analysis of the experimental outcomes, performed from Figures 12 and 13, it is concluded that unique identifiers spanning a character length \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Blockchain** & **Robustness to** & **Robustness to** & **Robustness to** & **Robustness to** \\  & **Storage Capacity** & **Geometric Attacks** & **Feature Addition/Deletion** & **Layer Addition/Deletion** & **Cropping and** \\ \hline BCPM-UI & Minimal & Yes & Yes & Yes & Yes \\ Ren & Significant & N/A & N/A & N/A & N/A \\ Lee & N/A & Yes & No & No & Yes \\ Li & N/A & Yes & No & No & No \\ Zhou & N/A & Yes & No & No & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison between the BCPM-UI model and the contrast model. of 1500-2000 meet the criteria for both uniqueness and robustness. Hence, they are deemed suitable for deployment on blockchain platforms. Figure 12: Experimental results of uniqueness between datasets with different unique identification lengths: (**a**) Shanghai dataset; (**b**) Beijing dataset; (**c**) Chengdu dataset; (**d**) Jiangsu dataset; (**e**) Hangzhou dataset; (**f**) Nanjing dataset.