# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 6 Security Analysis and Formal Verification
### Numerical Experiments and Validation  
To validate the blockchain deployment optimization and consensus mechanism, we construct a realistic and reliable test scenario. Real-time aircraft position data from the OpenSky Network platform [33] is used to simulate a practical ATM data-sharing environment. For blockchain deployment optimization, we adopt our proposed IF-NSGA-II algorithm with the following parameter settings: the number of objective functions is 3, the number of constraints is 3, the crossover probability is 0.85, and the mutation probability is 0.15. For the consensus algorithm, we use our proposed C-GPBFT, where the total number of nodes is set to 100 and the number of sub-clusters is 4. We compare IF-NSGA-II with the benchmark algorithm NSGA-II [34] in terms of solution quality and diversity. We randomly select one run result of two algorithms with a population size of 100 and visualize the non-dominated solution sets for the block allocation optimization problem. As shown in Figure 10, the x, y, and z axes represent the average weighted storage occupancy rate, average response time, and average unavailability probability, respectively. It can be seen that IF-NSGA-II achieves better coverage of the approximate non-dominated solution space than NSGA-II, with the approximate non-dominated solutions obtained by IF-NSGA-II exhibiting the widest distribution in the objective space. Furthermore, IF-NSGA-II contributes the most to the overall set of approximate non-dominated solutions, accounting for 92.24%, while NSGA-II contributes only 7.76%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & **Block Optimization** & **Searchable Data** & **Evalable Model** & **Multi-Level** \\  & **Deployment** & **Sharing** & **Sharing** & **Data-Sharing Model** \\ \hline Authentication & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ Authorization & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) \\ Privacy & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ Nash equilibrium & \(q_{1}q_{2}q_{4}Quit_{A}Quit_{R}\) & \(q_{1}q_{2}q_{4}q_{6}Quit_{R}\) & \(q_{1}q_{2}Quit_{S}Quit_{A}Quit_{R}\) & \(q_{1}q_{2}q_{3}q_{5}q_{7}\) \\ Security & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ requirements & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Security protocol game-theoretic analysis comparison. Figure 9: Game tree model analysis diagram. To demonstrate the advantages of C-GPBFT in ATM data sharing, we conduct a comparison with the traditional PBFT [35] algorithm. First, the aircraft nodes are divided into four sub-clusters, and the clustering results are shown in Figure 11. Then, we evaluate the consensus latency and throughput (TPS) of both algorithms, with the results presented in Figures 12 and 13, respectively. Figure 11: Clustering results with 4 sub-clusters (the total number of nodes is 100, and different colors represent different sub-clusters). Figure 10: Optimization results of IF-NSGA-II and NSGA-II with population size 100. Figure 12 presents a comparison of consensus latency between the two algorithms. In the prepare and commit phases of PBFT, each aircraft node in the consensus domain broadcasts messages to all other nodes in the network, resulting in a large number of communications for each consensus and consequently higher latency. In contrast, C-GPBFT adopts a clustering strategy that divides nodes into multiple sub-clusters based on their spatial distribution, significantly narrowing the consensus domain and reducing the consensus domain. In contrast, the consensus domain is not always a single node, but also a single node is not a single node. Figure 12: Consensus delay of C-GPBFT and PBFT in a dynamic cluster. Figure 13: Throughput of C-GPBFT and PBFT in a dynamic cluster. number of required communications.