## 4 Numerical Simulation Experiment

Table 5: Initial values of parameters in different scenarios. constant and the value of \(\Delta\) increases from 5 to 10, the evolutionary rates of both parties in the game model exhibited a significant decrease. (2) On the basis of the data in Table 5, Figure 5f was obtained by simulating the evolutionary trend of the game between users and the online health communities in Scenario 6. It can be seen from Figure 5f that the evolutionary results in this scenario are not unique, and there are two ESSs at the same time, namely, (0, 0) and (1, 1), i.e., users and OHCs choose (non-disclosure, negative protection) or (disclosure, positive protection). The simulation results in the above scenarios are consistent with the evolutionary process calculated by Proposition 3. It can be seen from Figure 5f that, from the beginning of the game, there are some users or communities that tend to both 0 and 1. We can see that in the early stage of the game, both sides are in a state of swing. Neither the sum of the benefits \(\Delta\) gained by users from disclosing private personal information nor the additional costs \(C_{2}\) paid by the communities to implement the \"positive protection\" strategy provide sufficient motivation for users or the communities to form a unified choice. Over time, two different evolutionary stable strategies (0, 0) and (1, 1) will eventually be formed. (3) On the basis of the data in Table 5, Figure 5g,h were obtained by simulating the evolutionary game trend of users and online health communities in Scenarios 7 and 8. It can be seen from Figure 5g,h that the ESSs of these scenarios are all (1, 0), i.e., users all choose to disclose private personal information and communities all choose to implement the \"negative protection\" strategy, which is consistent with the evolutionary process calculated by Proposition 4. Comparing g and h in Figure 5, it can be seen that, when \(\Delta>dL_{u}\) and other parameters are constant and the value of \(C_{2}\) decreases from 15 to 10, there is no significant difference in the evolutionary rate of users, while the evolutionary rate of the communities decreases significantly. It can be concluded that, when the sum of the user benefits is greater than the sum of the costs under the \"negative protection\" strategy, the drop in \(C_{2}\) has little effect on the users' strategy choice, but has a more significant impact on the communities' strategy. (4) On the basis of the data in Table 5, Figure 5i was obtained by simulating the evolutionary game trend of users and online health communities in Scenario 9. It can be seen from Figure 5i that the ESS of Scenario 9 is (1, 1), i.e., users choose to disclose their personal information and the communities choose to implement the \"positive protection\" strategy, which is consistent with the evolutionary process calculated by Proposition 5. In this scenario, users can obtain the rewards they want by disclosing their private personal information. Moreover, the communities can enhance the influence of the platform by implementing positive privacy protection strategies and reduce the losses caused by user privacy leakage. Consequently, this result is the optimal evolutionary stable strategy in all scenarios and it represents a \"win-win\" for both sides of the game simultaneously. On the basis of the above analysis, we found that the numerical simulation of Scenario 1 to 9 is completely consistent with the results calculated by the model. Therefore, it can be concluded that the evolutionary game model constructed in our research is accurate and effective. This also shows that the parameters involved in Formula (9) are decisive factors that can affect the decisions of both parties, such as \(\Delta\), \(Lu\), \(Lc\), \(C_{2}\), \(c\), and \(d\). Correspondingly, the parameters not involved in Formula (9) will not affect the evolutionary process, such as \(B\) and \(C_{1}\).