Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning

• URL: http://arxiv.org/abs/2105.07985v1
• Date: Mon, 17 May 2021 16:10:54 GMT
• Title: Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning
• Authors: Franziska Boenisch, Philip Sperl, Konstantin B\"ottinger
• Abstract summary: This paper experimentally evaluates the impact of training with Differential Privacy (DP) on model vulnerability against a broad range of adversarial attacks. The results suggest that private models are less robust than their non-private counterparts, and that adversarial examples transfer better among DP models than between non-private and private ones.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An important problem in deep learning is the privacy and security of neural networks (NNs). Both aspects have long been considered separately. To date, it is still poorly understood how privacy enhancing training affects the robustness of NNs. This paper experimentally evaluates the impact of training with Differential Privacy (DP), a standard method for privacy preservation, on model vulnerability against a broad range of adversarial attacks. The results suggest that private models are less robust than their non-private counterparts, and that adversarial examples transfer better among DP models than between non-private and private ones. Furthermore, detailed analyses of DP and non-DP models suggest significant differences between their gradients. Additionally, this work is the first to observe that an unfavorable choice of parameters in DP training can lead to gradient masking, and, thereby, results in a wrong sense of security.

Related papers

• Masked Differential Privacy [64.32494202656801]
  We propose an effective approach called masked differential privacy (DP), which allows for controlling sensitive regions where differential privacy is applied. Our method operates selectively on data and allows for defining non-sensitive-temporal regions without DP application or combining differential privacy with other privacy techniques within data samples.
  arXiv  Detail & Related papers  (2024-10-22T15:22:53Z)
• Privacy at a Price: Exploring its Dual Impact on AI Fairness [24.650648702853903]
  We show that differential privacy in machine learning models can unequally impact separate demographic subgroups regarding prediction accuracy. This leads to a fairness concern, and manifests as biased performance. implementing gradient clipping in the differentially private gradient descent ML method can mitigate the negative impact of DP noise on fairness.
  arXiv  Detail & Related papers  (2024-04-15T00:23:41Z)
• Discriminative Adversarial Privacy: Balancing Accuracy and Membership Privacy in Neural Networks [7.0895962209555465]
  Discriminative Adversarial Privacy (DAP) is a learning technique designed to achieve a balance between model performance, speed, and privacy. DAP relies on adversarial training based on a novel loss function able to minimise the prediction error while maximising the MIA's error. In addition, we introduce a novel metric named Accuracy Over Privacy (AOP) to capture the performance-privacy trade-off.
  arXiv  Detail & Related papers  (2023-06-05T17:25:45Z)
• Training Private Models That Know What They Don't Know [40.19666295972155]
  We find that several popular selective prediction approaches are ineffective in a differentially private setting. We propose a novel evaluation mechanism which isolate selective prediction performance across model utility levels.
  arXiv  Detail & Related papers  (2023-05-28T12:20:07Z)
• How Do Input Attributes Impact the Privacy Loss in Differential Privacy? [55.492422758737575]
  We study the connection between the per-subject norm in DP neural networks and individual privacy loss. We introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS) which allows one to apportion the subject's privacy loss to their input attributes.
  arXiv  Detail & Related papers  (2022-11-18T11:39:03Z)
• 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)
• 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)
• Robustness Threats of Differential Privacy [70.818129585404]
  We experimentally demonstrate that networks, trained with differential privacy, in some settings might be even more vulnerable in comparison to non-private versions. We study how the main ingredients of differentially private neural networks training, such as gradient clipping and noise addition, affect the robustness of the model.
  arXiv  Detail & Related papers  (2020-12-14T18:59:24Z)
• Private Reinforcement Learning with PAC and Regret Guarantees [69.4202374491817]
  We design privacy preserving exploration policies for episodic reinforcement learning (RL) We first provide a meaningful privacy formulation using the notion of joint differential privacy (JDP) We then develop a private optimism-based learning algorithm that simultaneously achieves strong PAC and regret bounds, and enjoys a JDP guarantee.
  arXiv  Detail & Related papers  (2020-09-18T20:18:35Z)

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.