# Efficient Multi-Source Anonymity for Aggregated Internet of Vehicles Datasets
## 5 Experimental Evaluation
### Data Utility  
In this subsection, we show the results of multi-source (\(k\),\(d\))-anonymity and multi-source (\(k\),\(l\),\(d\))-diversity in terms of data utility. We measure the information loss of the anonymized dataset using the global certainty penalty and discernibility metric. Figure 8a shows the GCP information loss metric for the Adult and Census datasets. Figure 8b shows the DM information loss metric. The parameters [k,d,l] are 20, 2, 3 and 200, 2, 3 on the adult and census datasets, respectively. The higher the GCP and DM, the greater the information loss. Multi-source (\(k\),\(d\))-anonymity and multi-source (\(k\),\(l\),\(d\))-diversity have a similar result on two different datasets for GCP and DM. Multi-source (\(k\),\(d\))-anonymity outperforms multi-source (\(k\),\(l\),\(d\))-diversity in that privacy constraints are easier to achieve. Figure 9a-d shows the variation in the information loss with increasing k values for the two algorithms. Figure 9a,b show the GCP metric on the Adult and Census datasets, respectively. Then, Figure 9c,d show the DM metric on the Adult and Census datasets, respectively. As shown in the figure, for all k values (10 <= k <= 20 for the Adult dataset and 100 <= k <= 200 \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{\(Q\)\(I_{1}\) **Set**} & \multicolumn{3}{c}{\(Q\)\(I_{2}\) **Set**} & \multirow{2}{*}{**Number of Equivalence Classes**} & \multicolumn{2}{c}{**Privacy Leakage**} & \multirow{2}{*}{**Privacy Leakage**} \\ \cline{2-2} \cline{4-9}  & **a1** & & & & & & & & & \\ \hline Mondrian [11] & 823 & 1679 & 2467 & 1534 & 1539 & 2583 & 2920 & 4969 & 5656 & 10,625 \\ \hline Incognito [10] & 0 & 0 & 0 & 0 & 76 & 0 & 76 & 76 & 152 \\ \hline Flash [45] & 1 & 818 & 818 & 818 & 818 & 818 & 818 & 1637 & 2454 & 4091 \\ \hline Our & 0 & 0 & 0 & 0 & 0 & 137 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Privacy disclosures of k-anonymity on Census dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{\(Q\)\(I_{1}\) **Set**} & \multicolumn{3}{c}{\(Q\)\(I_{2}\) **Set**} & \multirow{2}{*}{**Number of Equivalence Classes**} & \multicolumn{2}{c}{**Privacy Leakage**} & \multirow{2}{*}{**Privacy Leakage**} \\ \cline{2-2} \cline{4-9}  & **a1** & & & & & & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Top-down [44] \\ \end{tabular} } & \multirow{2}{*}{0} & \multirow{2}{*}{1206} & \multirow{2}{*}{1364} & \multirow{2}{*}{1098} & \multirow{2}{*}{1242} & \multirow{2}{*}{1175} & \multirow{2}{*}{1364} & \multirow{2}{*}{2570} & \multirow{2}{*}{3515} & \multirow{2}{*}{6085} \\ \hline Mondrian [11] & 2 & 696 & 646 & 122 & 622 & 538 & 987 & 1344 & 1282 & 2626 \\ \hline Incognito [10] & 0 & 0 & 0 & 0 & 0 & 0 & 15 & 0 & 0 & 0 \\ \hline Flash [45] & 1 & 18 & 184 & 18 & 184 & 6 & 184 & 203 & 208 & 411 \\ \hline Our & 0 & 0 & 0 & 0 & 0 & 388 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Privacy disclosures of k-anonymity on adult dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{\(Q\)\(I_{1}\) **Set**} & \multicolumn{3}{c}{\(Q\)\(I_{2}\) **Set**} & \multirow{2}{*}{**Number of Equivalence Classes**} & \multicolumn{2}{c}{**Privacy Leakage**} & \multirow{2}{*}{**Privacy Leakage**} \\ \cline{2-2} \cline{4-9}  & **a1** & & & & & & & & & \\ \hline Incognito [10] & 0 & 0 & 0 & 0 & 0 & 0 & 15 & 0 & 0 & 0 \\ \hline Flash [45] & 1 & 18 & 184 & 18 & 184 & 6 & 184 & 203 & 208 & 411 \\ \hline Our & 0 & 0 & 0 & 0 & 0 & 389 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Privacy disclosures of l-diversity on Adult dataset. for the Census dataset), these two algorithms have similar results in terms of GCP and DM. Meanwhile, the information loss of the two algorithms remains stable with increasing k values. In Figure 10a-d, we vary parameter d. That is the privacy parameter of multi-source (_k,d_)-anonymity and multi-source (_k,l,d_)-diversity.