**8. Performance Comparison of CMC with Existing Schemes**

The results of the proposed scheme CMC with existing schemes IndMZ [24] and TAPCS [25] in terms of ASS, entropy, and location traceability. The reason for choosing these two techniques is the similarity with our proposed scheme working with vehicle traffic conditions and used for privacy protection of vehicles in a road network environment. The values collected during simulations for anonymity set and entropy are given in Table 5. If a higher number of pseudonyms are changed by vehicles in a region, the target vehicle anonymisation is increased which ultimately increases entropy and confusion for an adversary to exploit the actual identity and location of a target vehicle. The location privacy scheme intends to create uncertainty in location information to generate confusion for an

adversary. Here, confusion means adding uncertainty in the vehicle location information for an adversary that makes it difficult to identify a vehicle by linking old pseudonyms with newly changed pseudonyms. The uncertainty can be produced by the proper pseudonyms changing process. The proposed scheme CMC improves entropy and increases confusion for an adversary when compared with existing schemes IndMZ [24] and TAPCS [25] as shown in the table. The average ASS is evaluated based on vehicle density and simulation time. Figures 12 and 13 show the proposed scheme's results in comparison to existing schemes for vehicle anonymisation. CMC improves vehicle anonymity over IndMZ and TAPCS. The reason behind this is the efficient management of vehicles that took part in the pseudonym update process. Initially, the anonymity is low due to a smaller number of transmission range vehicles. After some time, the anonymity of vehicles increases due to the successful change of pseudonyms. In Figure 13, the proposed scheme's behavior is slightly undulating due to the lack of vehicle interest in cooperation with neighbors in the region. However, the overall process moves towards improvement in the anonymisation of vehicles.


**Table 5.** Values collected during simulations.

**Figure 12.** Average anonymity versus time.

**Figure 13.** Anonymisation of vehicle identity at different vehicles' density.

Figure 14 shows the entropy of average anonymity set size at a certain time. The proposed scheme CMC beats the existing schemes TAPCS and IndMZ in terms of entropy. This shows the confusion generated concerning the target vehicle in the pseudonym updating process. CMC produces greater confusion than existing schemes for an adversary to find a target vehicle's actual identity in the communicating region. Ultimately, it improves the privacy protection level of vehicles. Similarly, in Figure 15, entropy is evaluated based on the number of vehicles took part in the pseudonym changing process. The entropy shows irregular behavior at different traffic densities; this is due to the lack of cooperation of vehicles in that region for the anonymisation process. CMC achieves higher confusion than TAPCS and IndMZ in various vehicle traffic. The achievement of the proposed CMC scheme regarding entropy is because of the efficient utilization of road context and pseudonym updating process. The IndMZ [24] generates fake pseudonyms in an individual manner and so the target vehicle can be easily identified by an adversary, which reduces the confusion level. The reduced performance of TAPCS compared with CMC regarding entropy is due to inefficient management of the pseudonym changing process in the silent period.

The vehicle tracking percentage during simulation time is shown in Figure 16. Initially, the proposed scheme CMC and TAPCS have similar tracking ratios at a certain amount of time. Over time, CMC reduces vehicle traceability for an adversary. The proposed scheme gets better results regarding reducing vehicle tracking probability compared with existing methods TAPCS [25] and IndMZ [24]. Similarly, tracking probability concerning the number of vehicles is shown in Figure 17. While there is a low number of vehicles at the start of the network, the tracking probability is also high. Whenever the number of vehicles is increasing, tracking probability is reduced to a certain level. CMC has a lower tracking percentage compared to existing schemes. The proposed scheme efficiently manages the road environment and makes use of any neighbor's cooperation in the pseudonym changing process, increasing the confusion for an adversary to track the pseudo-identities of vehicles.

**Figure 14.** Entropy with different periods.

**Figure 15.** Entropy with different vehicle traffic density.

**Figure 16.** Vehicle tracing probability.

**Figure 17.** Tracking probability of the number of vehicles.
