Differentially Private Synthetic Data Generation Using Context-Aware GANs

February 09, 2026

Reading time: 2 minute

...

📝 Original Info

Title: Differentially Private Synthetic Data Generation Using Context-Aware GANs
ArXiv ID: 2512.08869
Date: 2025-12-09
Authors: Anantaa Kotal, Anupam Joshi

📝 Abstract

The widespread use of big data across various sectors has brought significant privacy concerns, particularly when sensitive information is shared or analyzed. Regulations like GDPR and HIPAA impose strict controls on handling data, making it difficult to balance the need for insights with privacy requirements. Synthetic data offers a promising solution, enabling the creation of artificial datasets that mirror real-world patterns without exposing sensitive information. For instance, synthetic data can simulate patient records or network flows for training machine learning models to conduct research without violating privacy laws. However, traditional synthetic data generation methods often fail to capture complex, implicit rules that relate ...

📄 Full Content

In the era of big data, the ability to extract meaningful insights from vast and complex datasets has revolutionized fields as diverse as healthcare, security, finance, transportation, and marketing. Machine Learning (ML) models have become essential tools for analyzing these large-scale datasets, en-abling advancements in predictive modeling, decision-making, and automated systems. However, the utility of big data is often constrained by privacy and confidentiality concerns. Sharing sensitive data, such as medical records or financial transactions, raises significant privacy and security challenges, particularly when such data must cr

…(Content truncated for length.)

📄 Read Full PDF on ArXiv