With the explosive growth of data volumes, enterprises face unprecedented challenges in data governance. As a critical step in data modeling, multi-source data table merging enables the integration of multiple tables containing the same entities from different business system databases into a standardized entity table, thereby enhancing data quality and consistency. However, this process relies heavily on manual judgment, leading to inefficiencies. In recent years, the performance of large language models (LLMs) in natural language processing has provided new insights for addressing data governance issues. This paper proposes a "mutation-generation-tuning" approach that enhances the performance of small parameter LLMs (e.g., Baichuan2-7B) in multi-source data table merging tasks through secondary pre-training and fine-tuning. The specific methods include data augmentation and generating intermediate reasoning processes using large parameter LLMs. Experimental results indicate that the optimized MDTM-GPT model exhibits performance on par with high-parameter models in multi-source data table merging tasks while maintaining low cost and data security. The contributions of this paper lie in presenting a novel approach for small parameter LLMs and providing practical validation for applications in the field of data governance.

With the rapid growth of data and the increasing awareness of privacy protection, data privacy issues have become particularly important in the field of machine learning. Federated learning, as a distributed learning method, achieves collaborative training of models while preserving data privacy by keeping the data stationary and allowing the model to move. However, during the federated learning process, there is still a risk of privacy leakage when aggregating the intermediate parameters of models trained by different data providers. Researchers have found that adding noise to the intermediate parameters of the model using differential privacy can effectively prevent privacy inference on the data contributors. Nevertheless, there exists an inherent trade-off between the accuracy and privacy in federated learning models under differential privacy. Strengthening privacy protection often leads to a decrease in model performance. This trade-off becomes more pronounced in complex deep learning models that require multiple iterations to converge. To address the issues of data privacy, data silos, and the trade-off between data privacy leakage and model availability in deep learning within federated learning, this paper proposes a relaxed differential privacy federated learning approach. It reduces the impact of noise on the final results by selectively perturbing gradients when data providers return intermediate model parameters. Experiments demonstrate that this approach achieves a high level of accuracy while preserving data privacy. Additionally, it exhibits superior performance in terms of computational efficiency, striking a well-balanced compromise between accuracy and privacy.

As an essential means of privacy protection technology, zero-knowledge proof has gradually been applied into various fields with the development of blockchain technology, such as the Internet of Vehicles and Bitcoin. Personal comprehensive credit score as a measure to promote social governance is closely related to personal privacy. Although there are currently credit score calculation systems for various application scenarios, these systems almost ignore user privacy protection, which leads to user information leakage or abuse. The combination of zero-knowledge proof and personal credit score calculation has been studied by a number of researchers at the present stage. However, all data are provided by users directly in the current schemes, which did not consider the data authenticity under the situation that users provided false data. In this paper, we utilize zero-knowledge proof to design a novel privacy protection scheme for personal credit score calculation, taking into account the authenticity verification of multi-dimensional user data. In addition, our scheme also proposes the concept of a universal verification platform based on blockchain for personal credit scores. This platform has more substantial applicability and versatility for any qualified institution that requires querying and verifying user’s credit scores. At the end of the paper, we conducted a security analysis and performance evaluation for the overall scheme.

The transaction efficiency of blockchains has been a painful point haunting researchers all the time. The metrics of Transaction per Second (TPS) present a low value, especially as the number of network nodes increases. In order to improve the blockchain's TPS, numerous articles explored the solutions from multiple aspects, such as: using a new block system based on the Directed Acyclic Graph (DAG), adjusting the block structure, optimizing the consensus algorithm or adopting a sharding protocol. The sharding protocols play an extremely important role among these schemes, while the existing ones suffer from secure risks due to their static sharding. In this paper, we proposed a dynamic sharding protocol for consortium blockchains, employing random values to randomly partition the network. Our protocol aims for improving the throughput of the blockchain system as well as ensuring its security. The OMNET++ simulation results showed that, in the case of a large-scale consortium blockchain network of 400 nodes which is divided into 16 shards, the performance of our scheme is able to attain up to 50 times promotion, compared to the system without sharding.