Synthetic Data Generation and Differential Privacy using Tensor Networks' Matrix Product States (MPS)

• URL: http://arxiv.org/abs/2508.06251v1
• Date: Fri, 08 Aug 2025 12:14:57 GMT
• Title: Synthetic Data Generation and Differential Privacy using Tensor Networks' Matrix Product States (MPS)
• Authors: Alejandro Moreno R., Desale Fentaw, Samuel Palmer, Raúl Salles de Padua, Ninad Dixit, Samuel Mugel, Roman Orús, Manuel Radons, Josef Menter, Ali Abedi,
• Abstract summary: We propose a method for generating privacy-preserving high-quality synthetic data using Matrix Product States (MPS)<n>We benchmark the MPS-based generative model against state-of-the-art models such as CTGAN, VAE, and PrivBayes.<n>Our results show that MPS outperforms classical models, particularly under strict privacy constraints.
• Score: 33.032422801043495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic data generation is a key technique in modern artificial intelligence, addressing data scarcity, privacy constraints, and the need for diverse datasets in training robust models. In this work, we propose a method for generating privacy-preserving high-quality synthetic tabular data using Tensor Networks, specifically Matrix Product States (MPS). We benchmark the MPS-based generative model against state-of-the-art models such as CTGAN, VAE, and PrivBayes, focusing on both fidelity and privacy-preserving capabilities. To ensure differential privacy (DP), we integrate noise injection and gradient clipping during training, enabling privacy guarantees via R\'enyi Differential Privacy accounting. Across multiple metrics analyzing data fidelity and downstream machine learning task performance, our results show that MPS outperforms classical models, particularly under strict privacy constraints. This work highlights MPS as a promising tool for privacy-aware synthetic data generation. By combining the expressive power of tensor network representations with formal privacy mechanisms, the proposed approach offers an interpretable and scalable alternative for secure data sharing. Its structured design facilitates integration into sensitive domains where both data quality and confidentiality are critical.

Related papers

• On the MIA Vulnerability Gap Between Private GANs and Diffusion Models [51.53790101362898]
  Generative Adversarial Networks (GANs) and diffusion models have emerged as leading approaches for high-quality image synthesis.<n>We present the first unified theoretical and empirical analysis of the privacy risks faced by differentially private generative models.
  arXiv  Detail & Related papers  (2025-09-03T14:18:22Z)
• RL-Finetuned LLMs for Privacy-Preserving Synthetic Rewriting [17.294176570269]
  We propose a reinforcement learning framework that fine-tunes a large language model (LLM) using a composite reward function.<n>The privacy reward combines semantic cues with structural patterns derived from a minimum spanning tree (MST) over latent representations.<n> Empirical results show that the proposed method significantly enhances author obfuscation and privacy metrics without degrading semantic quality.
  arXiv  Detail & Related papers  (2025-08-25T04:38:19Z)
• Improving Noise Efficiency in Privacy-preserving Dataset Distillation [59.57846442477106]
  We introduce a novel framework that decouples sampling from optimization for better convergence and improves signal quality.<n>On CIFAR-10, our method achieves a textbf10.0% improvement with 50 images per class and textbf8.3% increase with just textbfone-fifth the distilled set size of previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-03T13:15:52Z)
• Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning [54.20871516148981]
  We introduce the Communication-Efficient and Privacy-Adaptable Mechanism (CEPAM)<n>CEPAM achieves communication efficiency and privacy protection simultaneously.<n>We theoretically analyze the privacy guarantee of CEPAM and investigate the trade-offs among user privacy and accuracy of CEPAM.
  arXiv  Detail & Related papers  (2025-01-21T11:16:05Z)
• SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy [0.0]
  We investigate capability of Large Language Models (Ms) to generate synthetic datasets with Differential Privacy (DP) mechanisms.<n>Our approach incorporates DP-based noise injection methods, including Laplace and Gaussian distributions, into the data generation process.<n>We then evaluate the utility of these DP-enhanced synthetic datasets by comparing the performance of ML models trained on them against models trained on the original data.
  arXiv  Detail & Related papers  (2024-12-30T01:10:10Z)
• Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy [64.32494202656801]
  Privacy-preserving computer vision is an important emerging problem in machine learning and artificial intelligence.<n>We present anonymization pipeline that replaces sensitive human subjects in video datasets with synthetic avatars within context.<n>We also proposeMaskDP to protect non-anonymized but privacy sensitive background information.
  arXiv  Detail & Related papers  (2024-10-22T15:22:53Z)
• Differentially Private Non Parametric Copulas: Generating synthetic data with non parametric copulas under privacy guarantees [0.0]
  This work focuses on enhancing a non-parametric co-pula-based synthetic data generation model, DPNPC, by incorporating Differential Privacy. We compare DPNPC with three other models (PrivBayes, DP-Copula, and DP-Histogram) across three public datasets, evaluating privacy, utility, and execution time.
  arXiv  Detail & Related papers  (2024-09-27T10:18:14Z)
• FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning [54.26614091429253]
  Federated instruction tuning (FedIT) is a promising solution, by consolidating collaborative training across multiple data owners. FedIT encounters limitations such as scarcity of instructional data and risk of exposure to training data extraction attacks. We propose FewFedPIT, designed to simultaneously enhance privacy protection and model performance of federated few-shot learning.
  arXiv  Detail & Related papers  (2024-03-10T08:41:22Z)
• Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning [3.29354893777827]
  We introduce a data release framework, 3A (Approximate, Adapt, Anonymize), to maximize data utility for machine learning. We present experimental evidence showing minimal discrepancy between performance metrics of models trained on real versus privatized datasets.
  arXiv  Detail & Related papers  (2023-07-04T18:37:11Z)
• 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)
• MC-GEN:Multi-level Clustering for Private Synthetic Data Generation [9.787793858206737]
  We propose MC-GEN, a privacy-preserving synthetic data generation method under differential privacy guarantee. MC-GEN builds differentially private generative models on the multi-level clustered data to generate synthetic datasets. Our results showed that MC-GEN can achieve significant effectiveness under certain privacy guarantees on multiple classification tasks.
  arXiv  Detail & Related papers  (2022-05-28T02:11:06Z)
• Just Fine-tune Twice: Selective Differential Privacy for Large Language Models [69.66654761324702]
  We propose a simple yet effective just-fine-tune-twice privacy mechanism to achieve SDP for large Transformer-based language models. Experiments show that our models achieve strong performance while staying robust to the canary insertion attack.
  arXiv  Detail & Related papers  (2022-04-15T22:36:55Z)
• Differentially Private Synthetic Medical Data Generation using Convolutional GANs [7.2372051099165065]
  We develop a differentially private framework for synthetic data generation using R'enyi differential privacy. Our approach builds on convolutional autoencoders and convolutional generative adversarial networks to preserve some of the critical characteristics of the generated synthetic data. We demonstrate that our model outperforms existing state-of-the-art models under the same privacy budget.
  arXiv  Detail & Related papers  (2020-12-22T01:03:49Z)
• 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)

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.