# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 5 Experimental Analysis
### Data Set  
The US-Census Adult dataset is derived from the UC Irvine Machine Learning Repository [45], which is composed of data accumulated from the US census. Data sets are described in Table 9. In our experiments, we extracted two independent datasets from the Adult dataset (i) Occupation and (ii) Education. The Adult dataset has 48842 tuples with six \(QI\) attribute values: Age, Sex, Marital status, Work class, Relationship, and Salary. Occupation is used as the sensitive attribute value for the Occupation dataset, and Education for the Education dataset.  
For the experiment, we needed independent datasets to simulate the independent data publishing environment. Therefore, 10 disjoint datasets were composed from each of the Education and Occupation datasets, each with 4 K randomly selected tuples. The remaining 8 K tuples were used to generate the overlapping tuples pool to check for composition attack. From the remaining tuples pool, we made five copies of each group, respectively inserting 100, 200, 300, 400, and 500 tuples into the Education and Occupation datasets. Finally, we obtained datasets with sizes of (4.1 K, 4.1 K), (4.2 K, 4.2 K), (4.3 K, 4.3 K), (4.4 K, 4.4 K) and (4.5 K, 4.5 K) for Education and Occupation datasets respectively.  
In the experiment, each group of datasets was used as input to the \(\epsilon-DP\)[41], Hybrid [2], Probabilistic [23], Composition [24], Mondrian [44] and Merging algorithms to calculate the privacy risks and corresponding data utility. Privacy-preserving multiple independent data publishing is a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Attribute** & **Type** & **Number of Values** \\ \hline
1 & Age & Continuous & 74 \\
2 & Sex & Categorical & 2 \\
3 & Marital status & Categorical & 7 \\
4 & Work class & Categorical & 8 \\
5 & Relationship & Categorical & 6 \\
6 & Salary & Categorical & 2 \\
7 & Occupation & Categorical & 14 \\
8 & Education & Categorical & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Description of US-Census Adult dataset.  
non-interactive data publishing context, and for experimental analysis, we conduct the experiment on the non-interactive privacy settings. Conversely, the majority of the work in differential privacy [37] follows the interactive settings and a user accesses a dataset through a numerical query, while the anonymization technique appends noise to the query answers. The interactive environments might not always support the entire situation because in most cases datasets need to be published in public. Therefore, we select a non-interactive setting for differential privacy experiment as discussed in [41].  
We compared the proposed Merging method with the \(\epsilon-DP\)[41], Hybrid [2], Probabilistic [23], Composition [24] and Mondrian [44] methods. To compute privacy risks, we execute all algorithms on the non-interactive privacy-preserving data publishing environment. In the non-interactive privacy settings, \(\epsilon-DP\), Hybrid, Probabilistic, Composition and Mondrian create _quasi-identifier_ equivalence class as \(k\)_-anonymity_ method [6]. For creating equivalence class, we select \(k=4\) and \(k=6\). In an equivalence class for the differential privacy, Laplacian noise is appended to the count of sensitive values [23]. To create the equivalence class for Merging method \(l\)_-diversity_[7] is chosen as discussed earlier, and we select \(l=4\) and \(l=6\). We select \(\epsilon=0.3\) for the \(\epsilon-differential\)_privacy_ budget.  
The Merging method creates \(n=1\) fake tuples. For this reason, the output size of the Merging method will be larger than the all other methods. In the experimental comparison, we have therefore calculated the percentage of the respective output for each group of datasets.  
In our experiments, we used the anonymization algorithm to anonymize the datasets. Composition attack was performed on all pairs of datasets, and data utility was measured after anonymization of the datasets.