Application of Multi-Party Computation in Data Security and Privacy Protection under the Context of Blockchain

Blockchain technology has significant implications for data security and privacy. However, the decentralized structure of blockchain networks poses challenges for ensuring data security and privacy. This study investigates the use of multi-party computation (MPC) to improve data security and privacy in blockchain environments. A comprehensive analysis of recent literature on blockchain security is conducted, highlighting various security vulnerabilities and proposed solutions. A novel methodology is proposed that integrates MPC and federated learning to mitigate data security and privacy concerns within blockchain networks. A consensus mechanism is designed to leverage MPC to achieve input privacy and output correctness, ensuring efficient and secure data processing. Furthermore, homomorphic encryption (HE) and differential privacy (DP) techniques are integrated to enhance data security and privacy. Experimental results validate the effectiveness of the proposed methodology in terms of computational efficiency, communication overhead, and security detection accuracy.