# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
## 6 Evaluation
### Simulation and Results
#### 6.3.2 Simulation Results  
We compare the other two algorithms (i.e., PLAM algorithm and LLB algorithm) with our SCTB algorithm. Figure (a)a shows the simulation results of response time under Scenario-1. It can be seen that with the number of users in a clustering group increases, the response time increases no matter which kind of algorithm is used. It is because more user leads more time to be spent. It can also be known that the response time of our SCTB is less than PLAM and LLB. That is because the computational complexity of our SCTB is smaller than PLAM and LLB. The reason why the response time of LLB is less than PLAM is the combination of some clustering groups when some certain condition is satisfied. And the condition is as follows: When the number of users in clustering group A is less than \(th_{k}/2\), the clustering group A can join in another clustering group B if and only if the clustering group B is within the communication range of A and the number of users in B is bigger than \(th_{k}/2\)[9]. Figure (b)b presents the relationship between the number of users and the success rate under Scenario-1. It can be seen that the success rate of our SCTB algorithm is higher than PLAM and LLB. That is because our SCTB algorithm leads the clustering process to choose new users selectively, and makes the clustering groups more reasonable by using the filtering aggregation protocol as analyzed in Section 5.4. Besides, the representative aggregation protocol promotes the success of clustering groups as well. The reason why the success rate of LLB is higher than PLAM is also the combination of some clustering groups. From Figure (b)b, it also can be known that no matter which kind of algorithm mentioned above is used in the clustering process, the success rate decreases as the number of users in a clustering group increases. The reason is that with the number of users in a clustering group increases (i.e., \(th_{k}\)), the clustering group has to spend more time to find users to meet the condition \(th_{k}\), which leads to the decrease of the success rate. Figure 11: The simulation results for Scenario-1. Figures 12 and 13 show the simulation results under Scenario-2 and Scenario-3 respectively. Figures 12a and 13a present the response time under Scenario-2 and Scenario-3 respectively. It can be seen that Figures 12a and 13a reflect a trend similar to Figure 11a, that is, the response time increases with the number of users in a clustering group increases. The response time of PLAM algorithm and LLB algorithm have almost the same performance in Scenario-1 and Scenario-2, which is influenced by the set ratio \(th_{l}=th_{k}/2\). However, in Figure 13a, with the number of users in a clustering group increases, the response time of PLAM and LLB increases faster than Figures 11a and 12a. The reason is, for a clustering group, the higher service category similarity of users leads a clustering group has to find more users to meet the condition \(th_{l}\), which will lead more time to be spent. Figures 12b and 13b present the success rate under Scenario-2 and Scenario-3 respectively. It can be seen that Figures 12b and 13b show a trend similar to Figure 11b, that is, the success rate decreases as the number of users in a clustering group increases. The success rate of PLAM algorithm and LLB algorithm nearly has the same performance in Scenario-1 and Scenario-2 due to the ratio \(th_{l}=th_{k}/2\). However, as can be seen from Figure 13b, with the higher service category similarity of users, our SCTB still has a better performance than PLAM and LLB. This is because our SCTB chooses new users selectively during the clustering process, and makes the clustering groups more reasonable in a region. Figure 14 shows the performance of the SCTB under Scenario-1, Scenario-2, and Scenario-3. Figure 14a shows the response time of SCTB under different scenarios. It can be seen that the response time increases with the number of users in a clustering group increases.