Enhancing User-Centric Privacy Protection: An Interactive Framework through Diffusion Models and Machine Unlearning

• URL: http://arxiv.org/abs/2409.03326v1
• Date: Thu, 5 Sep 2024 07:55:55 GMT
• Title: Enhancing User-Centric Privacy Protection: An Interactive Framework through Diffusion Models and Machine Unlearning
• Authors: Huaxi Huang, Xin Yuan, Qiyu Liao, Dadong Wang, Tongliang Liu,
• Abstract summary: The study pioneers a comprehensive privacy protection framework that safeguards image data privacy concurrently during data sharing and model publication. We propose an interactive image privacy protection framework that utilizes generative machine learning models to modify image information at the attribute level. Within this framework, we instantiate two modules: a differential privacy diffusion model for protecting attribute information in images and a feature unlearning algorithm for efficient updates of the trained model on the revised image dataset.
• Score: 54.30994558765057
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the realm of multimedia data analysis, the extensive use of image datasets has escalated concerns over privacy protection within such data. Current research predominantly focuses on privacy protection either in data sharing or upon the release of trained machine learning models. Our study pioneers a comprehensive privacy protection framework that safeguards image data privacy concurrently during data sharing and model publication. We propose an interactive image privacy protection framework that utilizes generative machine learning models to modify image information at the attribute level and employs machine unlearning algorithms for the privacy preservation of model parameters. This user-interactive framework allows for adjustments in privacy protection intensity based on user feedback on generated images, striking a balance between maximal privacy safeguarding and maintaining model performance. Within this framework, we instantiate two modules: a differential privacy diffusion model for protecting attribute information in images and a feature unlearning algorithm for efficient updates of the trained model on the revised image dataset. Our approach demonstrated superiority over existing methods on facial datasets across various attribute classifications.

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 Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models [112.48136829374741]
  In this paper, we unveil a new vulnerability: the privacy backdoor attack. When a victim fine-tunes a backdoored model, their training data will be leaked at a significantly higher rate than if they had fine-tuned a typical model. Our findings highlight a critical privacy concern within the machine learning community and call for a reevaluation of safety protocols in the use of open-source pre-trained models.
  arXiv  Detail & Related papers  (2024-04-01T16:50:54Z)
• Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models [11.59117790048892]
  This study tackles an unexplored practical privacy preservation use case by generating human-perceivable images that maintain accurate inference by an authorized model. Our results show that the generated images can successfully maintain the accuracy of a protected model and degrade the average accuracy of the unauthorized black-box models to 11.97%, 6.63%, and 55.51% on ImageNet, Celeba-HQ, and AffectNet datasets, respectively.
  arXiv  Detail & Related papers  (2024-02-14T17:11:52Z)
• $\alpha$-Mutual Information: A Tunable Privacy Measure for Privacy Protection in Data Sharing [4.475091558538915]
  This paper adopts Arimoto's $alpha$-Mutual Information as a tunable privacy measure. We formulate a general distortion-based mechanism that manipulates the original data to offer privacy protection.
  arXiv  Detail & Related papers  (2023-10-27T16:26:14Z)
• PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners [81.571305826793]
  We introduce Contextual Privacy Protection Language Models (PrivacyMind) Our work offers a theoretical analysis for model design and benchmarks various techniques. In particular, instruction tuning with both positive and negative examples stands out as a promising method.
  arXiv  Detail & Related papers  (2023-10-03T22:37:01Z)
• Diff-Privacy: Diffusion-based Face Privacy Protection [58.1021066224765]
  In this paper, we propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy. Specifically, we train our proposed multi-scale image inversion module (MSI) to obtain a set of SDM format conditional embeddings of the original image. Based on the conditional embeddings, we design corresponding embedding scheduling strategies and construct different energy functions during the denoising process to achieve anonymization and visual identity information hiding.
  arXiv  Detail & Related papers  (2023-09-11T09:26:07Z)
• Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification [15.382184404673389]
  The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions. Privacy-preserving mechanisms are paramount in medical image analysis, where the data being sensitive in nature. This study addresses the need by integrating differential privacy, a leading privacy-preserving technique, into a federated learning framework for medical image classification.
  arXiv  Detail & Related papers  (2023-06-30T16:48:58Z)
• Content-based Graph Privacy Advisor [38.733077459065704]
  We present an image privacy classifier that uses scene information and object cardinality as cues for the prediction of image privacy. Our Graph Privacy Advisor (GPA) model simplifies a state-of-the-art graph model and improves its performance.
  arXiv  Detail & Related papers  (2022-10-20T11:12:42Z)
• OPOM: Customized Invisible Cloak towards Face Privacy Protection [58.07786010689529]
  We investigate the face privacy protection from a technology standpoint based on a new type of customized cloak. We propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks. The effectiveness of the proposed method is evaluated on both common and celebrity datasets.
  arXiv  Detail & Related papers  (2022-05-24T11:29:37Z)
• Privacy Enhancement for Cloud-Based Few-Shot Learning [4.1579007112499315]
  We study the privacy enhancement for the few-shot learning in an untrusted environment, e.g., the cloud. We propose a method that learns privacy-preserved representation through the joint loss. The empirical results show how privacy-performance trade-off can be negotiated for privacy-enhanced few-shot learning.
  arXiv  Detail & Related papers  (2022-05-10T18:48:13Z)

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.