Privacy-Preserving Synthetic Data Generation Techniques

This whitepaper discusses the increasing demand for synthetic data generation in the context of artificial intelligence (AI) and machine learning. It outlines the challenges organizations face regarding data privacy, security, and regulatory compliance, emphasizing the need for privacy-preserving solutions. The paper details various techniques for generating synthetic data, including Generative Adversarial Networks (GANs), Conditional Tabular GAN (CTGAN), Variational Autoencoders (VAEs), and differentially private synthetic data methods. Each technique is explained in terms of its structure and functionality, highlighting how they contribute to creating high-quality synthetic datasets that mimic real-world data while protecting sensitive information. The document also addresses the problem of bias in AI training datasets and the role of synthetic data in mitigating these issues. Furthermore, it presents Mphasis' Synth Studio as a solution for generating and enriching synthetic data, emphasizing its commitment to innovation and privacy protection. The paper concludes by underscoring the importance of synthetic data in various industries and its potential to drive innovation while safeguarding data privacy.