Legal liability and compliance supervision mechanism in big data processing models

A new optimization framework is put forward, which is based on compliance and the quantitative index of legal responsibility. As for the method, the algorithm makes dynamic adjustment to the process by embedding the weight function of the compliance factor, so as to reach the equilibrium of law compliance and data processing efficiency. In the experiment section, we use a common dataset that includes many kinds of data to validate, and sets up a number of key performance metrics, such as compliance, processing efficiency, and computation cost. The simulation results show that the proposed algorithm can increase the compliance rate by 17% and the processing efficiency by 23%, and the computational cost is reduced by 15%. The results of the experiment show that the algorithm is stable and reliable. The results indicate that the new algorithm can not only meet the requirements of legal responsibility and compliance, but also improve the performance of large scale data processing.