Data Aggregation Mechanisms With Dynamic Integrity Trustworthiness Evaluation Framework for Datacenters

With the accelerated development of large models and distributed training, the explosive growth of data volume has brought huge challenges to traditional data processing and machine learning algorithms. The inconsistency and accuracy of the data processing process will directly affect the analysis and decision-making effect of the data. In this paper, we investigate a data fusion framework based on a trustworthiness metric model, aiming to incorporate a credibility evaluation mechanism of data sources into the data fusion process. Firstly, we propose a trust measurement model based on dynamic Bayesian networks that is correlated with time factors, taking into account the impact of network interaction behavior on trust measurement. Secondly, we design a data security aggregation mechanism based on the trustworthy measurement model, which combines credibility measurement with the minimum spanning tree (MST) protocol to improve the network's perception performance. Finally, we conduct simulation experiments and real experimental bed tests separately, and the results showed that the proposed model has continuous trustworthiness measurement ability in dynamic uncertain network environments. On a large benchmark dataset, the proposed algorithm performs better than state-of-the-art methods in statistics, with a significant reduction rate of 36.6% in the computational cost of the control center, and a reduction rate of 23.8% and 34.7% in the communication and storage costs of the system, respectively.