# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
## 2 Methods
### Canonical Data Identification
#### 2.1.3 Sensitivity Update Based on Association Rule Analysis  
Now we have preliminarily classified sensitivity attributes into sensitive and suspicious sensitive attribute sets. However, in practical scenarios, there often exist complex dependenciesamong attributes within each data table. Data thieves may exploit strong correlations between non-sensitive and sensitive attributes to infer or steal sensitive information.  
Therefore, it is necessary to further mine the strong association relationship between sensitive and suspicious sensitive attributes. By extracting association relationships among attributes in the system, we can recalculate the informativeness of each attribute based on the entropy change of the rule conditions. This enables us to update the sensitivity values accordingly and improve the overall effectiveness of sensitive attribute identification.  
Association rule mining [6] could capture the association relationship between different items and can be used to discover meaningful patterns from large datasets. Several widely used algorithms for association rule mining are as follows:  
* Apriori [19] is a widely used association rule mining algorithm that identifies strong association rules in a target dataset through an iterative candidate generation and pruning process.
* FP-Growth [20] compresses the dataset by constructing a data structure known as an FP-tree and generates frequent itemsets by traversing this tree. Strong association rules are then derived from the discovered frequent itemsets.
* Eclat [21] adopts a vertical data format and employs intersection operations to efficiently discover frequent itemsets, from which strong association rules are subsequently derived.  
Support and confidence are two fundamental metrics in association rule mining. Support measures the importance or frequency of an association within the dataset, while confidence evaluates the reliability or predictive strength of the association. The support and confidence of an association rule are calculated as follows:  
\[support(X\Rightarrow Y)=P(X\cup Y) \tag{4}\]  
\[Confidence(X\Rightarrow Y)=P(X\mid Y)=\frac{Support(X\cup Y)}{Support(X)} \tag{5}\]  
In practice, it is necessary to define thresholds for support and confidence. These thresholds help identify and retain strong association rules by filtering out those that do not meet the specified criteria. Strong association rules allow us to reclassify a subset of suspicious sensitive attributes into the sensitive attribute set. Attributes promoted from the suspicious sensitive set to the sensitive attribute set should have their sensitivity levels increased accordingly. Here, we update attribute sensitivity using mutual information between attributes and conditional transformation entropy.  
Mutual information [12], a fundamental concept in information theory, measures the degree of dependence between two random variables. A higher mutual information value indicates a stronger correlation. The mutual information between attributes \(X\) and \(Y\) is defined as follows:  
\[I(X,Y)=\sum_{x\in X}\sum_{y\in Y}p(x,y)\log_{2}\frac{p(x,y)}{p(x)p(y)} \tag{6}\]  
The changed conditional entropy [4] is defined as the sum of the original attribute's information entropy and the conditional entropy of the attributes with which it is correlated. The changed conditional entropy for attribute \(X\) is calculated as Equation (7). Attribute \(Y\) is the attribute that has a strong association with attribute \(X\).  
\[H_{z}(X)=H(X)+H(X\mid Y)=2*H(X)-I(X,Y) \tag{7}\]  
Based on the maximum discrete entropy and the changed conditional entropy, the sensitivity update formula for attribute \(X\) is defined as Equation (8). \(S_{X}\) represents the final sensitivity value of attribute \(X\), which serves as the basis for the subsequent clustering-based classification.  
\[S_{X}=\frac{H_{max}(X)-H_{z}(X)}{H_{max}(X)} \tag{8}\]