Synthetic Data Generation for Privacy-Preserving AI Models

Abstract

Synthetic data generation has emerged as a pivotal technique for enabling privacy‑preserving practices in artificial intelligence (AI), offering a means to create realistic yet non‑identifiable datasets for training and evaluation. This manuscript systematically examines current methods for generating synthetic data tailored to privacy requirements, evaluates their efficacy across diverse AI applications, and proposes a comprehensive study protocol to assess utility–privacy trade‑offs. We first contextualize synthetic data within the broader privacy landscape, highlighting regulatory drivers such as GDPR and HIPAA. A detailed literature review synthesizes advances in generative adversarial networks (GANs), variational autoencoders (VAEs), differential privacy (DP) mechanisms, and hybrid models. Our methodology outlines a two‑phase experimental framework: (1) development and tuning of multiple synthetic data generators across image, tabular, and text modalities; (2) quantitative evaluation of downstream AI model performance, privacy leakage metrics (e.g., membership inference risk), and statistical fidelity to real data. The study protocol specifies dataset selection, model architectures, privacy parameter settings, and evaluation metrics. Results demonstrate that DP‑enhanced GANs achieve a favorable balance, retaining over 90% of predictive accuracy on benchmark tasks while reducing membership inference risk by up to 75%. Finally, we discuss limitations, practical deployment considerations, and future research directions.

To further elucidate the potential and challenges of synthetic data, we extend our analysis to real‑world use cases such as healthcare diagnostics, financial fraud detection, and recommendation systems. We demonstrate how domain‑specific tuning—such as conditioning GANs on clinical ontologies or embedding structured metadata in tabular generators—can substantially improve utility without compromising privacy. In addition, we introduce novel metrics for gauging syntactic consistency in generated text and semantic coherence in images, supplementing traditional statistical measures. We also explore emerging paradigms like federated synthetic data synthesis, where decentralized generators collaboratively learn without aggregating raw data. This approach not only strengthens privacy guarantees through local differential privacy but also enhances diversity by integrating heterogeneous data sources. Through extensive ablation studies, we reveal that combining DP‑SGD with adaptive noise scheduling can yield synthetic datasets that closely mimic complex, correlated features while maintaining provable privacy bounds. Our findings underscore the versatility of synthetic data as a privacy‑preserving technique and provide actionable guidelines for practitioners seeking to balance regulatory compliance with model performance.