Design and Verification of Secure Multi-Party Computation Enhanced by AI Optimization

This paper proposes a multi-party secure computation model integrated with artificial intelligence optimization algorithms. By incorporating gradient descent and genetic algorithms, the model optimizes communication efficiency and computational performance in secure computation. The approach significantly reduces computational complexity and enhances privacy protection. Simulation experiments conducted in fields such as finance, healthcare, and the Internet of Things demonstrate an average reduction of 39.578% in communication overhead and a 67.342% improvement in computational efficiency. These results validate that the proposed model ensures security while achieving high computational efficiency and practical application value. The research offers a novel direction for the deep integration of multi-party secure computation and artificial intelligence.