Analyzing AI Models for Synthetic Data Generation in Privacy-Sensitive Machine Learning Applications
Keywords:
Synthetic Data, Privacy, Machine Learning, Generative Adversarial NetworksAbstract
The rise of machine learning (ML) applications has significantly advanced fields such as healthcare, finance, and social sciences, where privacy is of utmost concern. In these areas, synthetic data generation (SDG) models have emerged as an effective solution to overcome privacy constraints while ensuring that machine learning models can still be trained on useful datasets. This paper explores various AI-driven approaches to synthetic data generation, focusing on their applications in privacy-sensitive environments. By analyzing popular techniques, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and differential privacy-based methods, this paper discusses their potential to maintain data utility while protecting individual privacy. Furthermore, the challenges in evaluating the quality and privacy guarantees of synthetic data are explored. This paper aims to provide a comprehensive analysis of AI models used for synthetic data generation and their implications for machine learning in sensitive domains.
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References
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