Secure Multi-Party Computation for Collaborative Data Analysis in Network Security

Within the scope of this research, we provide a Secure Multi-Party Computation (SMPC) architecture that has been adapted specifically for the purpose of collaborative data analysis in network protection. Significant improvements have been made to the framework in terms of performance metrics, security, and applicability in the real world. When measured against a baseline, comparative analysis showed a notable reduction of 25% in execution time, a decrease of 20% in communication overhead, and a drop of 16.67% in computational complexity. These enhancements highlight the framework's efficiency advantages, which are vital for maximising responsiveness and scalability in collaborative efforts to improve network security. As a result of the security evaluation, which demonstrates a shift from moderate to high privacy preservation, strong resilience to assaults, and low sensitivity to parameters, the suggested framework has been shown to be reliable. Real-world application scenarios further emphasise its adaptability, showing that its influence on threat intelligence sharing is moderate, while its impact on collaborative incident response is significant, and its impact on vulnerability analysis is low. These findings, when taken as a whole, establish the SMPC framework as a useful resource for organisations that want to improve the effectiveness of their joint efforts to secure networked environments. The research places a strong emphasis on the practical importance of the framework, which helps to build enhanced trust, collaboration, and resilience against developing cyber threats. Assessments of scalability, improvements to cryptographic protocols, and interaction with newly developing technology are some potential future topics for study. Advanced threat analysis may also benefit.