Secure Synthetic Test Data Generation Using AI and Differential Privacy

Abstract

The paper examines the synthesis that is created on AI-based synthetic generations of test data, which would address the trade-offs between data secrecy, scale, and usefulness. As the privacy of data becomes a more critical issue, particularly in areas involving sensitive data, it is necessary to ensure that synthetic data can be used as a viable equivalent to real-world data and maintain levels of privacy. The data generation process incorporates the methods of differential privacy to avoid the leakage of personal data and can be regarded as the hoped-for resolution to privacy problems. The study explains the success of AI models, especially in generating high-quality synthetic data without compromising their usefulness. Among the key findings, the innovation of differential privacy is that it provides data confidentiality. Still, on issues of the preservation of maximized utility of the data, it suffers in large-scale settings. The study highlights the need to optimize these models to get a scalable, secure, and feasible solution to industries that utilize synthetic data to test and train AI-based systems.