Ungeneralizable Examples

• URL: http://arxiv.org/abs/2404.14016v1
• Date: Mon, 22 Apr 2024 09:29:14 GMT
• Title: Ungeneralizable Examples
• Authors: Jingwen Ye, Xinchao Wang,
• Abstract summary: Current approaches to creating unlearnable data involve incorporating small, specially designed noises. We extend the concept of unlearnable data to conditional data learnability and introduce textbfUntextbfGeneralizable textbfExamples (UGEs) UGEs exhibit learnability for authorized users while maintaining unlearnability for potential hackers.
• Score: 70.76487163068109
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The training of contemporary deep learning models heavily relies on publicly available data, posing a risk of unauthorized access to online data and raising concerns about data privacy. Current approaches to creating unlearnable data involve incorporating small, specially designed noises, but these methods strictly limit data usability, overlooking its potential usage in authorized scenarios. In this paper, we extend the concept of unlearnable data to conditional data learnability and introduce \textbf{U}n\textbf{G}eneralizable \textbf{E}xamples (UGEs). UGEs exhibit learnability for authorized users while maintaining unlearnability for potential hackers. The protector defines the authorized network and optimizes UGEs to match the gradients of the original data and its ungeneralizable version, ensuring learnability. To prevent unauthorized learning, UGEs are trained by maximizing a designated distance loss in a common feature space. Additionally, to further safeguard the authorized side from potential attacks, we introduce additional undistillation optimization. Experimental results on multiple datasets and various networks demonstrate that the proposed UGEs framework preserves data usability while reducing training performance on hacker networks, even under different types of attacks.

Related papers

• Pseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning [59.29849532966454]
  We propose PseudoProbability Unlearning (PPU), a novel method that enables models to forget data to adhere to privacy-preserving manner. Our method achieves over 20% improvements in forgetting error compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-11-04T21:27:06Z)
• Privacy-Preserving Debiasing using Data Augmentation and Machine Unlearning [3.049887057143419]
  Data augmentation exposes machine learning models to privacy attacks, such as membership inference attacks. We propose an effective combination of data augmentation and machine unlearning, which can reduce data bias while providing a provable defense against known attacks.
  arXiv  Detail & Related papers  (2024-04-19T21:54:20Z)
• Independent Distribution Regularization for Private Graph Embedding [55.24441467292359]
  Graph embeddings are susceptible to attribute inference attacks, which allow attackers to infer private node attributes from the learned graph embeddings. To address these concerns, privacy-preserving graph embedding methods have emerged. We propose a novel approach called Private Variational Graph AutoEncoders (PVGAE) with the aid of independent distribution penalty as a regularization term.
  arXiv  Detail & Related papers  (2023-08-16T13:32:43Z)
• What Can We Learn from Unlearnable Datasets? [107.12337511216228]
  Unlearnable datasets have the potential to protect data privacy by preventing deep neural networks from generalizing. It is widely believed that neural networks trained on unlearnable datasets only learn shortcuts, simpler rules that are not useful for generalization. In contrast, we find that networks actually can learn useful features that can be reweighed for high test performance, suggesting that image protection is not assured.
  arXiv  Detail & Related papers  (2023-05-30T17:41:35Z)
• RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense [9.806681555309519]
  Federated learning (FL) allows clients to collaboratively train a model without sharing their private data. Recent studies have shown that private information can still be leaked through shared gradients. We propose a user-configurable privacy defense, RecUP-FL, that can better focus on the user-specified sensitive attributes.
  arXiv  Detail & Related papers  (2023-04-11T10:59:45Z)
• Transferable Unlearnable Examples [63.64357484690254]
  Un unlearnable strategies have been introduced to prevent third parties from training on the data without permission. They add perturbations to the users' data before publishing, which aims to make the models trained on the published dataset invalidated. We propose a novel unlearnable strategy based on Classwise Separability Discriminant (CSD), which aims to better transfer the unlearnable effects to other training settings and datasets.
  arXiv  Detail & Related papers  (2022-10-18T19:23:52Z)
• Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage [9.83989883339971]
  Federated Learning (FL) framework brings privacy benefits to distributed learning systems. Recent studies have revealed that private information can still be leaked through shared information. We propose a new type of leakage, i.e., Generative Gradient Leakage (GGL)
  arXiv  Detail & Related papers  (2022-03-29T15:59:59Z)
• Do Gradient Inversion Attacks Make Federated Learning Unsafe? [70.0231254112197]
  Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. Recent works on the inversion of deep neural networks from model gradients raised concerns about the security of FL in preventing the leakage of training data. In this work, we show that these attacks presented in the literature are impractical in real FL use-cases and provide a new baseline attack.
  arXiv  Detail & Related papers  (2022-02-14T18:33:12Z)
• FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition [7.988454173034258]
  Federated Learning (FL) framework allows learning a shared model collaboratively without data being centralized or shared among data owners. We show in this paper that the generalization ability of the joint model is poor on Non-Independent and Non-Identically Distributed (Non-IID) data. We propose a novel boosting algorithm for FL to address both the generalization and gradient leakage issues.
  arXiv  Detail & Related papers  (2020-07-14T18:47:23Z)

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.