Research Article
A Secure Sharing Method for University Personnel Archive Data Based on Federated Learning
@INPROCEEDINGS{10.1007/978-3-031-50543-0_13, author={Xinwei Li and Yue Zhao and Min Zhou}, title={A Secure Sharing Method for University Personnel Archive Data Based on Federated Learning}, proceedings={Advanced Hybrid Information Processing. 7th EAI International Conference, ADHIP 2023, Harbin, China, September 22-24, 2023, Proceedings, Part I}, proceedings_a={ADHIP}, year={2024}, month={3}, keywords={Federated learning Archive data processing Data distribution Data sharing}, doi={10.1007/978-3-031-50543-0_13} }- Xinwei Li
Yue Zhao
Min Zhou
Year: 2024
A Secure Sharing Method for University Personnel Archive Data Based on Federated Learning
ADHIP
Springer
DOI: 10.1007/978-3-031-50543-0_13
Abstract
In response to the complex data trust evaluation process in the current process of secure sharing of university personnel archive data, which leads to long data encryption time and poor data sharing and distribution performance, a federated learning based method for secure sharing of university personnel archive data is proposed. Build a data federation learning module to provide a platform for subsequent data processing. Optimize federated learning algorithms and complete incremental federated learning of archive data. Federated incremental learning of archival data. Improve data privacy and security. Apply Kalman filtering technology and data mapping technology to achieve secure sharing of archival data. The experimental results show that this method can effectively reduce data encryption time and provide data sharing and distribution performance.
