# A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack
## 5 Performance Evaluation
### Simulation Results  
In this section, we evaluate the performance through simulation results. The experiment was written in Matlab, running in Intel Core i7-4610M 3.00 GHz CPU with 8 GB DDR memory of 64-bit Windows 7 operating system. We consider an environment that consists of tens of road sections. The user follows universal distribution and the user distribution degree \(\lambda\) varies from 2 to 4. We assume \(\alpha=3*l\), where \(\alpha\) stands for the number of roads in the candidate set. We evaluate the performance of our proposed scheme. Moreover, we compare the performance of our proposed scheme with some existing methods, such as the GA in [30], AR in [34], VW in [35] and exhaustive method. In [30], the cost function is considered a greedy approach(GA) is proposed. In [34], the adjacent road sections are preferentially added to the anonymous set. In [35], the tracking probability is considered and a voronoi map(VW) based algorithm is proposed. First, we show the performance of location privacy preserving. Then the cost of LBS and time consumption of generating an anonymous set are evaluated. From Figure 6, we can see the anonymous set parameters, such as the anonymous set size \(k\), road section number \(l\) and distribution degree \(\lambda\). It is obvious that all of the four schemes meet the demand of \(k\)-anonymity and \(l\)-diversity (\(k_{0}=10\), \(l_{0}=3\)). In other words, all the schemes can provide primary privacy protection for users. The anonymous set size and road section number of VW and our scheme are bigger than in GA and AR. It is because VW and our scheme consider the user distribution. In order to meet the needs of more strict privacy protection, more users and road sections are added to the anonymous set. Only our proposed scheme meets all the demands of privacy protection, including \(k\)-anonymity and \(l\)-diversity and distribution degree \(\lambda\). The proposed scheme can resist the side-weight inference attack and provide better privacy protection for the non-uniform distributed road network. Figure 7 gives the analysis of the tracking probability \(\epsilon\). We observe that the tracking probability would decrease with the increase of \(l_{0}\). With the increase of \(l_{0}\), there are more road sectors in the anonymous set. It becomes more difficult for the adversary to track the user. The tracking probability of VW and our scheme are smaller than that of GA and AR. It is because there are more road sectors in the anonymous set in VW and our scheme  
Figure 6: Anonymous set parameters. (\(k_{0}=10\), \(l_{0}=3\), \(\lambda_{0}=2\)). than in GA and AR, so as to meet the needs of more strict privacy protection. As the cost function is considered in our algorithm, the anonymous set size of our scheme is a bit smaller than in VW. Therefore, the tracking probability of our algorithm is a bit bigger than in VW. Our scheme achieves privacy protection with a smaller anonymous set. Figure 8 shows the distribution degree of anonymous set in different schemes. It is obvious that the scheme proposed in this paper meets the privacy protection requirements in all scenarios. In our proposed scheme, the road section with high user density is designed to be truncated. As a result, the distribution degree \(\lambda\) of the anonymous set decreases. Since the distribution of users tends to become uniform, it is hard for the adversary to track the user. Our proposed scheme can resist the side-weight inference attack. On the contrary, the other three schemes do not change the user distribution. The distribution degree \(\lambda\) of the anonymous set increase with the distribution degree of users in the map. The adversary would infer that the target user belongs to a high-density road section with high probability. The location privacy is not well protected against side-weight inference attacks. Figure 8: The distribution degree of anonymous set comparison with different user distribution degree. (\(k_{0}=10\), \(l_{0}=3\), \(\lambda_{0}=2\)).