@Article{cmes.2024.055596,
AUTHOR = {Chongzhen Zhang, Zhichen Liu, Xiangrui Xu, Fuqiang Hu, Jiao Dai, Baigen Cai, Wei Wang},
TITLE = {SensFL: Privacy-Preserving Vertical Federated Learning with Sensitive Regularization},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {142},
YEAR = {2025},
NUMBER = {1},
PAGES = {385--404},
URL = {http://www.techscience.com/CMES/v142n1/58973},
ISSN = {1526-1506},
ABSTRACT = {In the realm of Intelligent Railway Transportation Systems, effective multi-party collaboration is crucial due to concerns over privacy and data silos. Vertical Federated Learning (VFL) has emerged as a promising approach to facilitate such collaboration, allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training data. However, existing works have highlighted VFL’s susceptibility to privacy inference attacks, where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the client. This vulnerability poses a significant threat to VFL-based intelligent railway transportation systems. In this paper, we introduce <i>SensFL</i>, a novel privacy-enhancing method to against privacy inference attacks in VFL. Specifically, SensFL integrates regularization of the sensitivity of embeddings to the original data into the model training process, effectively limiting the information contained in shared embeddings. By reducing the sensitivity of embeddings to the original data, SensFL can effectively resist reverse privacy attacks and prevent the reconstruction of the original data from the embeddings. Extensive experiments were conducted on four distinct datasets and three different models to demonstrate the efficacy of SensFL. Experiment results show that SensFL can effectively mitigate privacy inference attacks while maintaining the accuracy of the primary learning task. These results underscore SensFL’s potential to advance privacy protection technologies within VFL-based intelligent railway systems, addressing critical security concerns in collaborative learning environments.},
DOI = {10.32604/cmes.2024.055596}
}