DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion

• URL: http://arxiv.org/abs/2310.04528v1
• Date: Fri, 6 Oct 2023 18:46:22 GMT
• Title: DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion
• Authors: Dongjie Chen, Sen-ching S. Cheung, Chen-Nee Chuah
• Abstract summary: We propose Differentially Private Data Publishing with Gaussian Optimized Model Inversion (DPGOMI) to address this issue. Our approach involves mapping private data to the latent space using a public generator, followed by a lower-dimensional DP-GAN with better convergence properties. Our results show that DPGOMI outperforms the standard DP-GAN method in terms of Inception Score, Freche't Inception Distance, and classification performance.
• Score: 8.204115285718437
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-dimensional data are widely used in the era of deep learning with numerous applications. However, certain data which has sensitive information are not allowed to be shared without privacy protection. In this paper, we propose a novel differentially private data releasing method called Differentially Private Data Publishing with Gaussian Optimized Model Inversion (DPGOMI) to address this issue. Our approach involves mapping private data to the latent space using a public generator, followed by a lower-dimensional DP-GAN with better convergence properties. We evaluate the performance of DPGOMI on standard datasets CIFAR10 and SVHN. Our results show that DPGOMI outperforms the standard DP-GAN method in terms of Inception Score, Fr\'echet Inception Distance, and classification performance, while providing the same level of privacy. Our proposed approach offers a promising solution for protecting sensitive data in GAN training while maintaining high-quality results.

Related papers

• Differentially Private Fine-Tuning of Diffusion Models [22.454127503937883]
  The integration of Differential Privacy with diffusion models (DMs) presents a promising yet challenging frontier. Recent developments in this field have highlighted the potential for generating high-quality synthetic data by pre-training on public data. We propose a strategy optimized for private diffusion models, which minimizes the number of trainable parameters to enhance the privacy-utility trade-off.
  arXiv  Detail & Related papers  (2024-06-03T14:18:04Z)
• LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification [67.92145284679623]
  We propose a DP-based tutor that models the noised private distribution and controls samples' generation with a low privacy cost. We theoretically analyze our model's privacy protection and empirically verify our model.
  arXiv  Detail & Related papers  (2024-02-26T11:52:55Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing [23.80837224347696]
  This paper proposes a privacy-preserving GPR method based on secret sharing (SS) We derive a new SS-based exponentiation operation through the idea of 'confusion-correction' and construct an SS-based matrix inversion algorithm based on Cholesky decomposition. Empirical results show that our proposed method can achieve reasonable accuracy and efficiency under the premise of preserving data privacy.
  arXiv  Detail & Related papers  (2023-06-26T08:17:51Z)
• Private Ad Modeling with DP-SGD [58.670969449674395]
  A well-known algorithm in privacy-preserving ML is differentially private gradient descent (DP-SGD) In this work we apply DP-SGD to several ad modeling tasks including predicting click-through rates, conversion rates, and number of conversion events. Our work is the first to empirically demonstrate that DP-SGD can provide both privacy and utility for ad modeling tasks.
  arXiv  Detail & Related papers  (2022-11-21T22:51:16Z)
• Private Set Generation with Discriminative Information [63.851085173614]
  Differentially private data generation is a promising solution to the data privacy challenge. Existing private generative models are struggling with the utility of synthetic samples. We introduce a simple yet effective method that greatly improves the sample utility of state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-07T10:02:55Z)
• DP2-Pub: Differentially Private High-Dimensional Data Publication with Invariant Post Randomization [58.155151571362914]
  We propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases. splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable privacy budget. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy.
  arXiv  Detail & Related papers  (2022-08-24T17:52:43Z)
• Differentially Private Generative Adversarial Networks with Model Inversion [6.651002556438805]
  To protect sensitive data in training a Generative Adversarial Network (GAN), the standard approach is to use differentially private (DP) gradient descent method. We propose Differentially Private Model Inversion (DPMI) method where the private data is first mapped to the latent space via a public generator. Our approach outperforms the standard DP-GAN method based on Inception Score, Fr'echet Inception Distance, and classification accuracy under the same privacy guarantee.
  arXiv  Detail & Related papers  (2022-01-10T02:26:26Z)
• 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)
• DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation [15.63770709526671]
  We propose a scalable privacy-preserving generative model DATALENS. We show that, DATALENS significantly outperforms other baseline DP generative models. We adapt the proposed TOPAGG approach, which is one of the key building blocks in DATALENS, to DP SGD training.
  arXiv  Detail & Related papers  (2021-03-20T06:14:19Z)

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.