# A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack
## 5 Performance Evaluation
### Simulation Results
Figure 7: The tracking probability \(\epsilon\) comparison with different query requirement \(l_{0}\). (\(k_{0}=10\), \(\lambda_{0}=2\)). The scheme proposed in this paper is designed to protect location privacy against side-weight inference attacks while reducing the cost as much as possible. In the following, the performance of the cost of LBS and time consumption of generating an anonymous set is evaluated. We compare the performance of our proposed scheme with VW and exhaustive method, which consider the non-uniform distributed users. Figure 9 shows the impact of privacy parameter \(l_{0}\) on LBS query cost. The LBS query cost increases along with the increase of privacy parameter \(l_{0}\). With the increase of \(l_{0}\), there are more users and road sectors in the anonymous set. Simultaneously, we can see that the cost of our algorithm is lower than VW. The scheme proposed in this paper is based on road truncation, where the road section with high user density is designed to be truncated. Based on Equation (5), the anonymous set in our algorithm is smaller than in VW and the LBS provider would respond fewer messages with a smaller anonymous set input. As a result, the cost of our proposed scheme is lower than in VW. Obviously, the cost of the exhaustive method is the smallest as it provides the optimal solution. The cost of our scheme is about 10% bigger than the exhaustive method. Our proposed scheme has reduced the cost of LBS effectively. Figure 10 shows the impact of privacy parameter \(l_{0}\) on the average time computation of generating an anonymous set. We can see that the time consumption would increase with the increase of \(l_{0}\). When the privacy requirement \(l_{0}\) increases, there will be more road sectors in the anonymous set. As a result, the time consumption to generate an anonymous set increases as it takes more time to decide which road sections is added to the anonymous set. The time consumption of our proposed scheme is lower than VW algorithm and the exhaustive method. The algorithm VW is based on the voronoi map of a road network, which consists of two stages. In the first stage, the road map is divided into serials of continuous polygons according to the user location. In the second stage, the anonymous set is generated. As a result, the algorithm VW takes a lot of time. As for the exhaustive method, it has to iterate through all the feasible solutions which satisfies the privacy protection requirements. Hence, it takes more time than our proposed scheme. Our scheme provides privacy protection within limit red time, which is applicable in LBS. In a conclusion, based on the above analysis, our proposed scheme not only satisfies the demand of \(k\)-anonymity and \(l\)-diversity but also has the ability to resist side-weight inference attacks. Meanwhile, the cost of LBS and time for generating an anonymous set is small. The scheme provides location privacy for users at a low cost. Figure 9: LBS query cost with different query requirement \(l_{0}\). (\(k_{0}=10\), \(\lambda_{0}=2\)).