Synthetic Data Generation and Differential Privacy using Tensor Networks' Matrix Product States (MPS)
This addresses data scarcity and privacy issues for sensitive domains requiring secure data sharing, offering an interpretable and scalable alternative, though it is incremental as it builds on existing tensor network and differential privacy methods.
The paper tackled the problem of generating high-quality synthetic tabular data with privacy guarantees, using Tensor Networks' Matrix Product States (MPS) and differential privacy techniques, and showed that MPS outperforms state-of-the-art models like CTGAN, VAE, and PrivBayes, especially under strict privacy constraints.
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 Rényi 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.