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 map** 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.
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Li, X., Zhao, Y., Zhou, M. (2024). A Secure Sharing Method for University Personnel Archive Data Based on Federated Learning. In: Yun, L., Han, J., Han, Y. (eds) Advanced Hybrid Information Processing. ADHIP 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-031-50543-0_13
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DOI: https://doi.org/10.1007/978-3-031-50543-0_13
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