# SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network
## 6 Experimental Evaluation
### Analysis of Experimental Results  
In order to verify the availability of SSKM_DP, MDAV [16], IDP_KMENAS [17], MDAV_DP [18], and SSKM_DP algorithms were compared on two data, respectively.  
In the experiment, the value of the privacy budget \(\varepsilon\) was [0.05, 0.1, 1, 5], and the number of data attributes m was [5; 10]. In order to decrease the error caused by the experiment, 20 experiments were carried out for the four algorithms on two data, respectively, and the average value of the 20 experiments was taken as the final experimental result.  
For the UCI Adult data, the number of different attributes of \(m\) was set to evaluate the data utility through the SSKM_DP algorithm. The experimental results of data utility are shown in Figure 4.  
As can be observed in Figure 5, for Adult data, when the value of \(\varepsilon\) increased from 0.05 to 5, the value of information loss MSE decreased gradually. When \(\varepsilon\) was 0.05, the MSE value changed slightly with the increase of the number of clustering \(a\), but the MSE value was still very low, indicating that the availability of data released through the SSKM_DP algorithm was also very low when the intake noise was very high. When the value of \(\varepsilon\) was [1, 5], the value of MSE was large and the availability of data was greatly improved. Since the clustering scale of the SFLA-Kohonen network is not subject to artificial constraints, it is completely dependent on the network topology mapping relationships. As the number of clusters \(a\) increased, the MSE value did not change significantly. Therefore, the SSKM_DP algorithm has a good anti-noise performance.  
For the UCI Adult data, the number of different attributes of m was set to evaluate the privacy protection ability of the SSKM_DP algorithm. The experimental results of the privacy protection ability are shown in Figure 6.  
As can be seen from Figure 6, for the Adult data, when the value of \(\varepsilon\) was {0.05, 0.1}, even if the value of attribute number m and cluster number a changed, the value of the record correlation RL did not change significantly. When the value of \(\varepsilon\) was 5, the value of RL also increased with the increase of a, the risk of information disclosure gradually increased, and the ability of privacy protection decreased. As can be seen from Figures 4 and 5, when the privacy budget \(\varepsilon\) was 1 and 5, the SSKM_DP algorithm had good data utility. When \(\varepsilon\) was 1, the privacy protection ability of SSKM_DP algorithm was much better than that of \(\varepsilon\) as 5. Therefore, this paper took \(\varepsilon=1\) as the optimal privacy budget value of the SSKM_DP algorithm.  
For NLTCS and UCI Adult data, the value of the privacy budget \(\varepsilon\) was set as 1, and the number of different attributes of m was taken. The SSKM_DP algorithm compares with MDAV [25], IDP_KMENAS [26], and MDAV_DP [27]. Experimental results on information loss are shown in Figure 7.  
Figure 5: Change trend of SSE for the Adult dataset.  
Figure 6: Change trend of RL for the Adult data.  
As can be seen from Figure 7, for Adult and NLTCS data, when \(\varepsilon\) was 1 and the number of attributes \(m\) was 5 and 10, respectively, the MSE of MDAV, IDP_KMENAS, and MDAV_DP gradually decreased with the increase of the number of clustering \(a\), while the MSE of the SSKM_DP algorithm remained stable all the time. The MSE of SSKM_DP was always smaller than the MSE of the other three algorithms. This is because MDAV, IDP_KMENAS, and MDAV_DP are very sensitive to the number of clustering \(a\), resulting in uneven clustering results and considerable information loss, while SSKM_DP is not affected by the number of clustering \(a\) and has a good anti-noise ability.  
It can be clearly concluded from the experimental results that the published data generated by the SSKM_DP algorithm are obviously better than MDAV, IDP_KMENAS, and MDAV_DP in terms of data utility when the privacy protection degree is certain.