SECURITY IMPLICATIONS OF SYNTHETIC DATA GENERATION: MEMBERSHIP INFERENCE AND MODEL LEAKAGE RISKS
Subjects/Theme:
Synthetic Data, Membership Inference Attack, Model Leakage, Privacy Risk, Generative Models, Differential Privacy, Data Security, GANs, VAE, Machine Learning SecurityDescription
Security and Privacy in AI Systems,
Edited By: Dr. Sunita Chaudhary, Dr. Joydeb Patra
ISBN (978-81-685212-9-2)
Synthetic data generation has emerged as a widely adopted privacy-preserving technique in machine learning, particularly in sensitive domains such as healthcare, finance, and social sciences. By replacing real datasets with artificially generated samples, organizations aim to mitigate direct privacy risks. However, recent advances reveal that synthetic data is not inherently secure. This study critically examines the security implications of synthetic data generation, focusing on membership inference attacks (MIAs) and model leakage risks. Membership inference allows adversaries to determine whether specific records were part of the training dataset, thereby compromising privacy. Additionally, generative models such as GANs and VAEs may inadvertently memorize and reproduce sensitive patterns, leading to data leakage. Through a synthesis of recent empirical studies, this paper analyzes attack mechanisms, evaluates vulnerabilities across different synthetic data paradigms, and discusses mitigation strategies including differential privacy and regularization. The findings highlight that while fully synthetic data offers improved protection, partially synthetic and overfitted models remain highly vulnerable. The study concludes by proposing a risk-aware framework for balancing privacy and utility in synthetic data deployment.
