DP$^2$-VAE: Differentially Private Pre-trained Variational Autoencoders

• URL: http://arxiv.org/abs/2208.03409v1
• Date: Fri, 5 Aug 2022 23:57:34 GMT
• Title: DP$^2$-VAE: Differentially Private Pre-trained Variational Autoencoders
• Authors: Dihong Jiang, Guojun Zhang, Mahdi Karami, Xi Chen, Yunfeng Shao, Yaoliang Yu
• Abstract summary: We propose DP$2$-VAE, a training mechanism for variational autoencoders (VAE) with provable DP guarantees and improved utility via emphpre-training on private data. We conduct extensive experiments on image datasets to illustrate our superiority over baselines under various privacy budgets and evaluation metrics.
• Score: 26.658723213776632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern machine learning systems achieve great success when trained on large datasets. However, these datasets usually contain sensitive information (e.g. medical records, face images), leading to serious privacy concerns. Differentially private generative models (DPGMs) emerge as a solution to circumvent such privacy concerns by generating privatized sensitive data. Similar to other differentially private (DP) learners, the major challenge for DPGM is also how to achieve a subtle balance between utility and privacy. We propose DP$^2$-VAE, a novel training mechanism for variational autoencoders (VAE) with provable DP guarantees and improved utility via \emph{pre-training on private data}. Under the same DP constraints, DP$^2$-VAE minimizes the perturbation noise during training, and hence improves utility. DP$^2$-VAE is very flexible and easily amenable to many other VAE variants. Theoretically, we study the effect of pretraining on private data. Empirically, we conduct extensive experiments on image datasets to illustrate our superiority over baselines under various privacy budgets and evaluation metrics.

Related papers

• Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning [16.028575596905554]
  We propose Differentially Private Prototype Learning (DPPL) as a new paradigm for private transfer learning. DPPL generates prototypes that represent each private class in the embedding space and can be publicly released for inference. We show that privacy-utility trade-offs can be further improved when leveraging the public data beyond pre-training of the encoder.
  arXiv  Detail & Related papers  (2024-06-12T09:41:12Z)
• Privacy Amplification for the Gaussian Mechanism via Bounded Support [64.86780616066575]
  Data-dependent privacy accounting frameworks such as per-instance differential privacy (pDP) and Fisher information loss (FIL) confer fine-grained privacy guarantees for individuals in a fixed training dataset. We propose simple modifications of the Gaussian mechanism with bounded support, showing that they amplify privacy guarantees under data-dependent accounting.
  arXiv  Detail & Related papers  (2024-03-07T21:22:07Z)
• Unlocking Accuracy and Fairness in Differentially Private Image Classification [43.53494043189235]
  Differential privacy (DP) is considered the gold standard framework for privacy-preserving training. We show that pre-trained foundation models fine-tuned with DP can achieve similar accuracy to non-private classifiers.
  arXiv  Detail & Related papers  (2023-08-21T17:42:33Z)
• Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano [83.5933307263932]
  We study data reconstruction attacks for discrete data and analyze it under the framework of hypothesis testing. We show that if the underlying private data takes values from a set of size $M$, then the target privacy parameter $epsilon$ can be $O(log M)$ before the adversary gains significant inferential power.
  arXiv  Detail & Related papers  (2022-10-24T23:50:12Z)
• TAN Without a Burn: Scaling Laws of DP-SGD [70.7364032297978]
  Differentially Private methods for training Deep Neural Networks (DNNs) have progressed recently. We decouple privacy analysis and experimental behavior of noisy training to explore the trade-off with minimal computational requirements. We apply the proposed method on CIFAR-10 and ImageNet and, in particular, strongly improve the state-of-the-art on ImageNet with a +9 points gain in top-1 accuracy.
  arXiv  Detail & Related papers  (2022-10-07T08:44:35Z)
• Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent [69.14164921515949]
  We characterize privacy guarantees for individual examples when releasing models trained by DP-SGD. We find that most examples enjoy stronger privacy guarantees than the worst-case bound. This implies groups that are underserved in terms of model utility simultaneously experience weaker privacy guarantees.
  arXiv  Detail & Related papers  (2022-06-06T13:49:37Z)
• Large Scale Transfer Learning for Differentially Private Image Classification [51.10365553035979]
  Differential Privacy (DP) provides a formal framework for training machine learning models with individual example level privacy. Private training using DP-SGD protects against leakage by injecting noise into individual example gradients. While this result is quite appealing, the computational cost of training large-scale models with DP-SGD is substantially higher than non-private training.
  arXiv  Detail & Related papers  (2022-05-06T01:22:20Z)
• Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence [73.14373832423156]
  We propose DP-Sinkhorn, a novel optimal transport-based generative method for learning data distributions from private data with differential privacy. Unlike existing approaches for training differentially private generative models, we do not rely on adversarial objectives.
  arXiv  Detail & Related papers  (2021-11-01T18:10:21Z)
• DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing [0.0]
  We propose DPlis--Differentially Private Learning wIth Smoothing. We show that DPlis can effectively boost model quality and training stability under a given privacy budget.
  arXiv  Detail & Related papers  (2021-03-02T06:33:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.