# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 7 Limitations  
Although the proposed multi-layered secure sharing framework for aviation big data demonstrates promising theoretical performance in terms of security, privacy, and efficiency, several practical challenges and limitations remain, which require further exploration and refinement in future research.  
First, the integration of blockchain with existing ATM data systems presents a significant challenge. Current aviation systems rely heavily on legacy infrastructures and standardized interfaces, yet no unified blockchain protocols or interfacing standards have been established for this domain. Large-scale system renovations may incur high costs and technical complexity, particularly within globally interconnected ATM networks. Future work may consider introducing middleware technologies to bridge blockchain with traditional infrastructures, thereby minimizing disruption and improving deployment feasibility.  
Second, energy consumption remains an important concern. Although this work adopts a consortium blockchain to avoid the high energy costs of public chains, the collaborative learning scenario involving frequent interactions and multi-party participation still incurs non-negligible computational overhead. In light of global efforts toward green aviation and carbon reduction, subsequent research should explore more energy-efficient consensus mechanisms such as lightweight protocols and investigate edge-cloud collaborative architectures to optimize resource allocation.  
Third, regulatory and legal adaptation for blockchain in aviation is still lacking. Currently, there is no well-established global regulatory framework for blockchain applications in civil aviation. Cross-border data sharing raises issues such as data sovereignty, accountability, and jurisdictional ambiguity. Future work could explore the design of auditable smart contracts, compliance-oriented templates, and participant authorization mechanisms. Collaboration with international bodies such as ICAO may be necessary to establish common standards and facilitate regulatory alignment.  
Fourth, ethical issues must not be overlooked when deploying federated learning in critical infrastructures such as ATM systems. When federated learning models are involved in safety-critical decision-making, challenges may arise during model collaboration, including model bias, lack of decision transparency, and ambiguous responsibility attribution. If participants' data contain structural biases, it can adversely affect the overall model's performance and fairness. Moreover, since model updates and decision processes are mainly conducted locally without a unified auditing mechanism, future work should explore mechanisms such as explainable models and auditable smart contracts to enhance system trustworthiness.  
Fifth, the deployment of blockchain-based data-sharing mechanisms on a global scale also faces significant data governance challenges. In the international aviation environment, cross-jurisdictional data flows may trigger issues related to compliance with data movement regulations, responsibility attribution, and the delineation of regulatory authority.  
Currently, there is a lack of unified standards and protocols for cross-border inter-chain data governance. Subsequent research can integrate technologies such as verifiable privacy-preserving computation to build interoperable data governance frameworks, supporting secure and efficient international collaboration.  
In summary, the architecture proposed in this paper provides a valuable exploratory path toward secure and trustworthy data sharing in ATM systems. However, further improvement and development are needed in areas including standardization, energy efficiency optimization, regulatory adaptation, ethical application, and cross-border data governance.