Differential Privacy Regularization: Protecting Training Data Through Loss Function Regularization
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 11 references that are in the map. First posted on arXiv 25 Sep 2024.
Builds on
7 of 11 references.
References · 7
- 2016Deep Learning with Differential Privacycited by 2,682
- 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
- 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 179
- 2024A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systemscited by 74
- 2022Privately Fine-Tuning Large Language Models with Differential Privacycited by 39
- 2024Fine-Tuning Large Language Models with User-Level Differential Privacycited by 28
- 2022On the utility and protection of optimization with differential privacy and classic regularization techniquescited by 10
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Citing papers per year: 2025: 1.
1 paper.