## 6 Conclusions  
This study performed classification methods of machine learning for predicting judicial decisions and analyzed the network. With machine learning models, it is usually \begin{table}
\begin{tabular}{c c c} \hline \hline
**Main Keyword** & **Degree Centrality** &
\begin{tabular}{c} **Network of Privacy Invasion** \\ **with Innocence** \\ \end{tabular} \\ \hline Adware & 0.143 & \\ Cyberattack & 0.085 & \\ Malware & 0.221 & \\ Spam & 0.142 & \\ Spyware & 0.429 & \\ Vandalism & 0.222 & \\ Virus & 0.102 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Degree Centrality of Innocence. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Main Keyword** & **Degree Centrality** &
\begin{tabular}{c} **Network of Privacy Invasion** \\ **with Compensation** \\ \end{tabular} \\ \hline Adware & 0.173 & \\ Cyberattack & 0.198 & \\ Malware & 0.231 & \\ Vandalism & 0.098 & \\ Virus & 0.102 & \\ Spam & 0.200 & \\ Spyware & 0.211 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Degree Centrality of Compensation. very hard to explain how each factor influences the establishment of a model. Thus, this study constructed the network of judicial cases according to the privacy invasion factors by NTA. We could identify which model had the highest performance rate of prediction of legal decisions among the five classification algorithms and infer how these algorithms can predict legal or illegal activities by analyzing the network that was established by NTA. The results of this study can be summarized as follows. First, the CART technique had the best performance of classification compared to the other methods. It can be inferred that CART algorithms should be considered a priority among various prediction algorithms for establishing the system of an AI lawyer or an automatic judicial decision machine. In other words, as CART shows the best performance within the scope of online privacy cases used in this study, it is desirable to adopt CART to predict legal disputes and judgments related to online privacy. These results have the potential to be used not only in online privacy disputes but also in resolving various legal conflicts that may occur in the online environment. However, since there is no guarantee that CART always shows the highest performance rate in predicting the guilt or innocence of numerous off-line crimes or in predicting legal decisions (e.g., probation, penalty) not covered in this study, other models should also be considered. Secondly, we can infer that malware is the most significant factor for predicting illegal decisions among various online privacy invasions. It is thereby derived that the malware node has the largest effects in the whole conviction network. If a firm invades the user's personal information based on malware for direct or indirect reasons in the process of carrying out marketing activities, there is a high chance of it resulting in conviction. Furthermore, malware can be also judged as being needed with compensation of damage in a perspective of civil law. In the case of viruses, if a firm is not able to prove that it did not create the relevant malicious code, or is not the primary distribution agent, it could result in conviction. As a result of analyzing legal precedents related to privacy invasion in the US, this study arrived at the following implications. Since it is difficult for firms to prove that they have no fault in the user's privacy invasion, it is necessary to establish drastic technical measures and management systems to prevent these issues in advance. Civil judgment of compensation is the most effective means of relief for users whose privacy is invaded by corporate activities. In particular, if invasions were made by sending spyware, virus, malware, adware, and spam, users can demand compensation for damages if they can prove the firm's negligence. Firms' obligations for accidents and users' probability for winning a lawsuit differ according to type of online privacy invasion.