标题： 合成数据生成和使用张量网络的矩阵乘积态（MPS）的差分隐私 显示英文标题

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

摘要： 合成数据生成是现代人工智能中的关键技术，解决了数据稀缺性、隐私约束以及训练健壮模型所需的多样化数据集问题。 在本工作中，我们提出了一种使用张量网络生成隐私保护的高质量合成表格数据的方法，特别是矩阵乘积态（MPS）。 我们将基于MPS的生成模型与最先进的模型如CTGAN、VAE和PrivBayes进行基准测试，重点关注保真度和隐私保护能力。 为了确保差分隐私（DP），我们在训练过程中集成了噪声注入和梯度裁剪，通过Rényi差分隐私会计实现隐私保证。 在分析数据保真度和下游机器学习任务性能的多个指标中，我们的结果表明，MPS在严格隐私约束下优于经典模型。 这项工作突显了MPS作为隐私感知合成数据生成的有前途工具。 通过将张量网络表示的表达能力与正式的隐私机制相结合，所提出的方法为安全数据共享提供了一个可解释且可扩展的替代方案。 其结构化设计便于集成到数据质量和保密性至关重要的敏感领域中。 显示英文摘要

摘要： 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.