Auto DP-SGD: Dual Improvements of Privacy and Accuracy via Automatic Clipping Threshold and Noise Multiplier Estimation

• URL: http://arxiv.org/abs/2312.02400v1
• Date: Tue, 5 Dec 2023 00:09:57 GMT
• Title: Auto DP-SGD: Dual Improvements of Privacy and Accuracy via Automatic Clipping Threshold and Noise Multiplier Estimation
• Authors: Sai Venkatesh Chilukoti, Md Imran Hossen, Liqun Shan, Vijay Srinivas Tida, and Xiai Hei
• Abstract summary: DP-SGD has emerged as a popular method to protect personally identifiable information in deep learning applications. We propose an Auto DP-SGD that scales the gradients of each training sample without losing gradient information. We demonstrate that Auto DP-SGD outperforms existing SOTA DP-SGD methods in privacy and accuracy on various benchmark datasets.
• Score: 1.7942265700058988
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: DP-SGD has emerged as a popular method to protect personally identifiable information in deep learning applications. Unfortunately, DP-SGD's per-sample gradient clipping and uniform noise addition during training can significantly degrade model utility. To enhance the model's utility, researchers proposed various adaptive DP-SGD methods. However, we examine and discover that these techniques result in greater privacy leakage or lower accuracy than the traditional DP-SGD method, or a lack of evaluation on a complex data set such as CIFAR100. To address these limitations, we propose an Auto DP-SGD. Our method automates clipping threshold estimation based on the DL model's gradient norm and scales the gradients of each training sample without losing gradient information. This helps to improve the algorithm's utility while using a less privacy budget. To further improve accuracy, we introduce automatic noise multiplier decay mechanisms to decrease the noise multiplier after every epoch. Finally, we develop closed-form mathematical expressions using tCDP accountant for automatic noise multiplier and automatic clipping threshold estimation. Through extensive experimentation, we demonstrate that Auto DP-SGD outperforms existing SOTA DP-SGD methods in privacy and accuracy on various benchmark datasets. We also show that privacy can be improved by lowering the scale factor and using learning rate schedulers without significantly reducing accuracy. Specifically, Auto DP-SGD, when used with a step noise multiplier, improves accuracy by 3.20, 1.57, 6.73, and 1.42 for the MNIST, CIFAR10, CIFAR100, and AG News Corpus datasets, respectively. Furthermore, it obtains a substantial reduction in the privacy budget of 94.9, 79.16, 67.36, and 53.37 for the corresponding data sets.

Related papers

• Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach [62.000948039914135]
  Using Differentially Private Gradient Descent with Gradient Clipping (DPSGD-GC) to ensure Differential Privacy (DP) comes at the cost of model performance degradation. We propose a new error-feedback (EF) DP algorithm as an alternative to DPSGD-GC. We establish an algorithm-specific DP analysis for our proposed algorithm, providing privacy guarantees based on R'enyi DP.
  arXiv  Detail & Related papers  (2023-11-24T17:56:44Z)
• Sparsity-Preserving Differentially Private Training of Large Embedding Models [67.29926605156788]
  DP-SGD is a training algorithm that combines differential privacy with gradient descent. Applying DP-SGD naively to embedding models can destroy gradient sparsity, leading to reduced training efficiency. We present two new algorithms, DP-FEST and DP-AdaFEST, that preserve gradient sparsity during private training of large embedding models.
  arXiv  Detail & Related papers  (2023-11-14T17:59:51Z)
• Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD [56.01810892677744]
  We show a connection between per-sample gradient norms and the estimation bias of the private gradient oracle used in DP-SGD. We propose Bias-Aware Minimisation (BAM) that allows for the provable reduction of private gradient estimator bias.
  arXiv  Detail & Related papers  (2023-08-23T09:20:41Z)
• Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy [67.33715954653098]
  We propose a novel DPFL algorithm named DP-FedSAM, which leverages gradient perturbation to mitigate the negative impact of DP. Specifically, DP-FedSAM integrates Sharpness Aware of Minimization (SAM) to generate local flatness models with stability and weight robustness. To further reduce the magnitude random noise while achieving better performance, we propose DP-FedSAM-$top_k$ by adopting the local update sparsification technique.
  arXiv  Detail & Related papers  (2023-05-01T15:19:09Z)
• DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling [23.8561225168394]
  differential privacy (DP) has become a well-accepted standard for privacy protection, and deep neural networks (DNN) have been immensely successful in machine learning. A classic mechanism for this purpose is DP-SGD, which is a differentially private version of the gradient descent (SGD) commonly used for training. We propose DPIS, a novel mechanism for differentially private SGD training that can be used as a drop-in replacement of the core of DP-SGD.
  arXiv  Detail & Related papers  (2022-10-18T07:03:14Z)
• Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints [20.533039211835902]
  We design a general framework that uses aggregates of intermediate checkpoints emphduring training to increase the accuracy of DP ML techniques. We demonstrate that training over aggregates can provide significant gains in prediction accuracy over the existing state-of-the-art for StackOverflow, CIFAR10 and CIFAR100 datasets. Our methods achieve relative improvements of 0.54% and 62.6% in terms of utility and variance, on a proprietary, production-grade pCVR task.
  arXiv  Detail & Related papers  (2022-10-04T19:21:00Z)
• Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization [94.06564567766475]
  DP-SGD and DP-NSGD mitigate the risk of large models memorizing sensitive training data. We show that these two algorithms achieve similar best accuracy while DP-NSGD is comparatively easier to tune than DP-SGD.
  arXiv  Detail & Related papers  (2022-06-27T03:45:02Z)
• Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger [39.93710312222771]
  Per-example clipping is a key algorithmic step that enables practical differential private (DP) training for deep learning models. We propose an easy-to-use replacement, called automatic clipping, that eliminates the need to tune R for any DPs.
  arXiv  Detail & Related papers  (2022-06-14T19:49:44Z)
• 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)
• DP-FP: Differentially Private Forward Propagation for Large Models [2.062295244789704]
  We show how to mitigate the performance drop by replacing the Differential Private Gradient Descent with a novel DP Forward-Propagation (DP-FP) Our DP-FP achieves an average accuracy of 91.34% with privacy budgets less than 3, representing a 3.81% performance improvement over the state-of-the-art DP-SGD.
  arXiv  Detail & Related papers  (2021-12-29T07:32:29Z)
• Dynamic Differential-Privacy Preserving SGD [19.273542515320372]
  Differentially-Private Gradient Descent (DP-SGD) prevents training-data privacy breaches by adding noise to the clipped gradient during SGD training. The same clipping operation and additive noise across training steps results in unstable updates and even a ramp-up period. We propose the dynamic DP-SGD, which has a lower privacy cost than the DP-SGD during updates until they achieve the same target privacy budget.
  arXiv  Detail & Related papers  (2021-10-30T04:45:11Z)

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.