# Secure and Transparent Space Exploration Data Management Using a Hybrid Blockchain Model
## 5 Conclusions  
In this paper, we propose a hybrid blockchain model to ensure the security and transparency of space exploration data in multinational collaborations. Our approach addresses the trade-off between transparency and confidentiality by combining the openness of public blockchains with the restricted access control of private blockchains. Data integrity is preserved through cryptographic hashing and zero-knowledge proofs, enabling the verification of even sensitive information without revealing its content. We also introduced a decentralized cross-chain synchronization mechanism using Chainlink's CCIP, mitigating the trust and failure risks associated with centralized oracles. A prototype system was implemented using Ethereum and Hyperledger Fabric, and validated with real extravehicular activity data from the United States and Russia. The results show that the system effectively detects data tampering, enforces role-based access control, and supports trustworthy collaboration between organizations. Compared to conventional centralized databases, the hybrid blockchain approach provides enhanced data security, auditability, and resistance to unauthorized modification. Compared to pure private blockchains, which offer strong access control but lack transparency and verifiability across organizations, the hybrid model enhances auditability through public blockchain anchoring while maintaining data privacy. Similarly, compared to consortium blockchains that rely on mutual trust among a limited set of entities, our approach reduces the risks of collusion or unintentional data manipulation by enabling an immutable, publicly auditable ledger. This combination of transparency and confidentiality makes the hybrid model particularly suitable for multinational space collaborations where trust boundaries are complex. Although blockchain platforms naturally exhibit lower throughput and higher \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Attack Scenario** & **Attack Vector** & **Potential Impact** & **Mitigation Strategy** \\ \hline Cross-chain synchronization attack & Data tampering during cross-chain communication & Incorrect data recorded on blockchain & Digital signatures, duplicate verification protocols \\ \hline Insider attack via unauthorized access & Privilege misuse by authorized insiders & Sensitive data leakage & Role-based access control, multi-signature, real-time monitoring \\ \hline \hline \end{tabular}
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Table 5: Summary of attack scenarios and mitigation strategies. latency, our experimental findings indicate that the performance remains acceptable for typical space mission data management scenarios. These findings align with broader observations on the potential of blockchain technologies in distributed systems [24]. However, several limitations should be noted. Another important limitation is that the current evaluation focuses on short-term system behavior. Over prolonged operational periods, factors such as blockchain storage growth could increase data retrieval times and transaction costs. Additionally, the latency associated with zero-knowledge proof generation and verification may accumulate as the volume of protected data expands. Future work should investigate long-term performance scalability strategies, including storage pruning, data compression techniques, and the adoption of efficient zk-SNARK frameworks to mitigate potential degradation in system responsiveness. And there is a lack of precise quantification of energy consumption associated with zero-knowledge proof generation and blockchain operations. Although the computational latency was manageable, the energy efficiency of the system under extended deployment remains unexplored. Considering the constrained power environments typical of space missions, future work should include detailed profiling of energy usage during proof generation, data synchronization, and smart contract execution, with the goal of optimizing resource consumption.