Differential Privacy Regularization: Protecting Training Data Through Loss Function Regularization
<|reference_start|>Differential Privacy Regularization: Protecting Training Data Through Loss Function Regularization: Training machine learning models based on neural networks requires large datasets, which may contain sensitive information. The models, however, should not expose private information from these datasets. Differentially private SGD [DP-SGD] requires the modification of the standard stochastic gradient descent [SGD] algorithm for training new models. In this short paper, a novel regularization strategy is proposed to achieve the same goal in a more efficient manner.<|reference_end|>
@article{aguilera-martínez2024differential,
    title={Differential Privacy Regularization: Protecting Training Data Through
  Loss Function Regularization},
    author={Francisco Aguilera-Mart'inez, Fernando Berzal},
    journal={arXiv preprint arXiv:2409.17144},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17144},
    primaryClass={cs.LG cs.AI cs.CR cs.NE}
}