Big Data Privacy Protection and Secure Sharing Mechanism Based on Blockchain
Haotian Miao · 2025
Purpose: This research addresses critical limitations in existing blockchain-based data sharing solutions by developing an innovative framework integrating zero-knowledge proofs, homomorphic encryption, and smart contract automation for comprehensive big data privacy protection while maintaining utility and regulatory compliance. Methodology: A hierarchical distributed architecture comprising four layers was designed: data owner layer for encryption, blockchain network layer for consensus, privacy protection layer for cryptographic protocols, and application service layer for user interactions. Experimental evaluation was conducted on distributed networks with 20-100 nodes processing$100 ~\text{GB}-5 ~\text{TB}$datasets. Findings: The proposed framework achieves$\text{9 4. 1 \%}$privacy protection strength with$\text{2 2 \%}$computational efficiency improvement compared to existing approaches. The system supports 100 -node deployments while maintaining 131-158 TPS throughput, significantly outperforming traditional zero-knowledge implementations that achieve only 89.3 % privacy strength. Conclusion: The framework represents significant advancement in blockchain-based big data privacy protection, successfully balancing security guarantees with computational efficiency. Practical Implications: The solution demonstrates substantial value for healthcare, financial services, and IoT applications requiring secure collaborative analytics and enterprise-scale data sharing scenarios.