## 3 Access Control Policy Combination Method for Data Fusion in Railway Traffic Systems
### Object Sensitivity  
Data categorization and hierarchization are based on a qualitative analysis of the data content, which is relatively static and cannot reflect the influence of data in the case of malicious access. Therefore, it is necessary to raise a sensitivity indicator to dynamically evaluate the impact of an object on the other entities in the whole system through previous interactions. In a multilayer rail transit system, we take a data table as the basic unit of access control object, and its importance depends on utilization and correlation, which are evaluated based on information entropy.  
Utilization refers to the frequency at which an object is accessed externally. By comparing the frequency of all objects, it can be observed that core business data are typically accessed more frequently with higher utilization, so the consequence is severer in the case of misuse, indicating higher sensitivity. Let \(K_{i}\) represent the set of subjects accessing object  
Figure 3: Access control framework for data sharing in a multilayer rail transit system based on ABAC.  
at time period \(t\), and function \(count(K_{i})\) represents the number of elements in set \(K_{i}\). The connectivity information entropy of \(i\) is defined as \(I_{i}^{c}\):  
\[I_{i}^{c}=-\sum_{j\in K_{j}(t)}\frac{1}{count(K_{j})+1}lb\frac{1}{count(K_{j})+1} \tag{1}\]  
Let \(F_{ij}\) be the number of interactions between subject \(j\) and object \(i\). The interaction frequency information entropy of \(i\) is defined as \(I_{i}^{f}\):  
\[I_{i}^{f}=-\sum_{j\in K_{j}}\frac{F_{ij}}{\sum\limits_{k\in K_{i}}F_{ik}+1}lb \frac{F_{ij}}{\sum\limits_{k\in K_{j}}F_{ik}+1} \tag{2}\]  
Therefore, the data utilization \(H_{i}^{u}\) is the product of its connectivity information entropy \(I_{i}^{c}\) and the interaction frequency information entropy \(I_{i}\), given by:  
\[H_{i}^{U}=I_{i}^{c}*I_{i}^{f} \tag{3}\]  
B. Correlation.  
Object correlation refers to the frequency at which an object is associated with other objects. If subject \(k\) accesses both objects \(i\) and \(j\) during time period \(t\), i.e., \(K_{ij}=K_{i}\cap K_{j}\
eq\phi\), then objects \(i\) and \(j\) are considered to be correlated and form a path \(R_{ij}\) with subject \(k\), which is constructed by the order of accessed objects by subject \(k\). A path may pass through one or more objects apart from \(i\) and \(j\), thus the correlation information entropy between objects \(i\) and \(j\) is:  
\[H_{ij}^{c}=\sum_{k=1}^{N}\frac{\theta+H_{i}^{U}}{\prod\limits_{m\in R_{k}} \left(\theta+H_{m}^{U}\right)} \tag{4}\]  
where \(N\) refers to the number of paths between \(i\) and \(j\), and \(\theta\) represents the influence of the network environment, assuming that the entity is not connected to others and has its inherent influence when calculating indirect influence. Here, \(\theta\) is set to 1 [15].  
The sensitivity of an object should be evaluated based on both its utilization and correlation:  
\[S_{ij}=\alpha H_{i}^{U}+(1-\alpha)H_{ij}^{c} \tag{5}\]  
where \(\alpha\in[0,1]\) is the coefficient for the influence of utilization on the total sensitivity. For any new entity, its correlation with other entities is not of much significance, thus we have \(\alpha=1\). If the accessing the subject receives feedback information due to previous malicious behavior, its trust will be decreased. For a core business object \(i\) with extremely high secure hierarchy, its utilization may be low. However, as the data interaction proceeds, the sensitivity of its associated objects is experiencing rapid increase due to frequency related, so its correlation is quite significant, indicating a non-negligible sensitivity on the whole.