# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 2 Related Work  
As the value of aviation data resources becomes increasingly prominent, establishing secure, trustworthy, and efficient collaborative data-sharing mechanisms has become a research focus. In recent years, blockchain has gained widespread application in privacy-sensitive domains due to its decentralized, tamper-proof, and traceable characteristics. In the ATM field, existing research has attempted to overcome the trust deficiencies of centralized architectures. For example, ATMChain [4] integrates identity authentication, access control, and data sharing into a unified 4A security model (Authentication, Authorization, Accounting, Auditing). While it enhances trustworthiness and auditability, its consensus mechanism lacks real-time adaptability to high-frequency ATM data. Another blockchain-based distributed flight data platform [5] improves data consistency and availability under the SESAR and SWIM frameworks but remains largely conceptual, lacking in-depth technical implementation. In healthcare [6], cloud storage [7], Internet of Things (IoT) [8], and intelligent transportation [9], blockchain technology has also been widely applied. For instance, HSHB [10] achieves secure and efficient data access by dividing private and consortium chain sharing, constructing access control policies and B+ tree indexing, and combining proxy re-encryption with smart contracts. Reference [8] proposes a multi-center IoT data-sharing framework that reduces computational overhead through improved attribute-based encryption and cloud-side obfuscation strategies. The integration of blockchain and federated learning (FL) is building a new paradigm for trusted privacy-preserving computing. Reference [11] proposes a blockchain-FL energy trading framework based on edge computing in 5G environments, while [12] designs a medical privacy-preserving architecture based on FL and blockchain. However, existing research primarily focuses on horizontal federated learning, with limited support for vertical federated learning (VFL), which is crucial for cross-institutional ATM data sharing. Additionally, there is a lack of quantitative fairness evaluation mechanisms for multi-party collaboration contributions. Consensus mechanisms are the core of blockchain-enabled trusted collaboration, with the key challenge being how to ensure system security and consistency in untrusted, heterogeneous, and dynamic network environments. Byzantine Fault Tolerance (BFT) protocols achieve fault tolerance and robustness in malicious node environments through cryptographic verification, redundant communication, and voting mechanisms. Existing solutions are mainly divided into proof-based (e.g., PoW, PoS) and election-based (e.g., PBFT). Proof-based schemes offer strong decentralization in open networks but struggle to meet ATM system requirements for strong consistency and low latency due to uncertain outcomes and high delays. PBFT, suitable for semi-trusted environments, enables deterministic consensus and is widely adopted. However, PBFT faces O(n\({}^{2}\)) communication complexity and a leader node bottleneck. To improve PBFT performance, various optimization schemes have been proposed: the work in [13] introduces a dynamic PBFT ordering service with bandwidth reservation, optimizing throughput and cluster scalability; reference [13] allows each ordering node to autonomously initiate consensus and employs a bandwidth reservation protocol to achieve efficient distributed ledger record insertion in permissioned blockchain. Reference [14] designs a scalable multi-layer PBFT consensus mechanism, reducing communication complexity through hierarchical node grouping to support large-scale systems. The authors of [15] adopt threshold proxy signatures and a dual-layer reputation mechanism to enable the TP-PBFT protocol, which achieves consensus even when more than 1/3 of nodes fail. However, existing schemes mostly assume stable connectivity and fixed topologies, making them unsuitable for high-mobility, weak-link scenarios like ATM communications.