Deep Learning with Gaussian Differential Privacy (1911.11607v3)

Abstract: Deep learning models are often trained on datasets that contain sensitive information such as individuals' shopping transactions, personal contacts, and medical records. An increasingly important line of work therefore has sought to train neural networks subject to privacy constraints that are specified by differential privacy or its divergence-based relaxations. These privacy definitions, however, have weaknesses in handling certain important primitives (composition and subsampling), thereby giving loose or complicated privacy analyses of training neural networks. In this paper, we consider a recently proposed privacy definition termed \textit{$f$-differential privacy} [18] for a refined privacy analysis of training neural networks. Leveraging the appealing properties of $f$-differential privacy in handling composition and subsampling, this paper derives analytically tractable expressions for the privacy guarantees of both stochastic gradient descent and Adam used in training deep neural networks, without the need of developing sophisticated techniques as [3] did. Our results demonstrate that the $f$-differential privacy framework allows for a new privacy analysis that improves on the prior analysis~[3], which in turn suggests tuning certain parameters of neural networks for a better prediction accuracy without violating the privacy budget. These theoretically derived improvements are confirmed by our experiments in a range of tasks in image classification, text classification, and recommender systems. Python code to calculate the privacy cost for these experiments is publicly available in the \texttt{TensorFlow Privacy} library.

• The paper introduces f-DP to provide closed-form privacy bounds during neural network training, surpassing traditional (ε,δ)-DP techniques.
• The research demonstrates that f-DP yields stronger privacy guarantees with optimization methods like SGD and Adam across tasks including image and text classification.
• The enhanced utility from f-DP enables reduced noise injection during training, significantly improving predictive performance while maintaining privacy thresholds.

Overview of "Deep Learning with Gaussian Differential Privacy"

The paper entitled "Deep Learning with Gaussian Differential Privacy" by Zhiqi Bu, Jinshuo Dong, Qi Long, and Weijie J. Su addresses the increasing need for privacy-preserving deep learning models. These models often use sensitive datasets, making it crucial to consider privacy measures such as Differential Privacy (DP) or its variations. This paper proposes using a novel privacy definition known as $f$-differential privacy ($f$-DP) to provide a more refined analysis for training neural networks while enhancing prediction accuracy.

The focus is on overcoming the limitations of classical $(\epsilon, \delta)$-DP with respect to handling composition and subsampling. The authors build on a framework that employs $f$-DP, which facilitates more precise privacy guarantees when utilizing algorithms like Stochastic Gradient Descent (SGD) and Adam in deep learning. The research demonstrates substantial improvements over previous methods and supports this with both theoretical findings and empirical evidence across a variety of tasks such as image and text classification, as well as recommender systems.

Key Contributions

1. Closed-Form Privacy Bounds: The use of $f$-DP provides analytically tractable expressions for privacy guarantees without needing complex techniques, unlike those used in prior work, like the moments accountant.
2. Performance Analyses: Through rigorous analysis, $f$-DP shows stronger privacy guarantees even under the $(\epsilon, \delta)$-DP framework. This improvement aligns with theoretical predictions as it accurately captures privacy loss during neural network training.
3. Utility Enhancement: The enhanced privacy analysis enables trading some degree of privacy for notable gains in utility, thus improving the overall predictive performance of the models by reducing noise injection during training while maintaining the privacy threshold.

Implications and Future Directions

The implications of adopting $f$-DP in deep learning frameworks are comprehensive. By achieving tighter privacy bounds, it opens avenues for training high-accuracy models under stricter privacy constraints. This advancement is particularly beneficial when dealing with sensitive data in healthcare, finance, and social networks.

Moving forward, research can explore the utility of $f$-DP in other machine learning paradigms and assess its scalability across different architectures or datasets. Additionally, integrating $f$-DP with adaptive learning strategies could potentially enhance model accuracy whilst still respecting differential privacy. Another interesting direction is expanding the use of $f$-DP beyond neural networks to other forms of machine learning models, potentially setting a new standard in privacy-preserving data analysis.

Conclusion

This paper makes a significant step toward realizing effective privacy-preserving neural network training by leveraging $f$-DP. Its ability to provide a more granular privacy guarantee offers substantial improvements in maintaining data privacy without compromising on model performance. As deep learning applications continue to permeate sectors dependent on sensitive data, the adoption of such refined privacy measures will become increasingly crucial to aligning technological advancements with ethical standards.