PSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization
arXiv:2606.18518v1 Announce Type: cross Abstract: The development of medical AI is constrained by limited access to high-quality clinical data due to institutional silos and strict privacy regulations such as HIPAA and GDPR. Synthetic data generation offers a potential solution, but existing methods lack principled mechanisms to explicitly manage the privacy-utility trade-off, often degrading clinically meaningful patterns or risking patient re-identification. We present PSyGenTAB, a privacy-preserving generative framework that formulates synthetic healthcare data generation as a constrained o
The increasing demand for medical AI, coupled with stringent privacy regulations like HIPAA and GDPR, is creating an urgent need for privacy-preserving data solutions.
This development addresses a critical bottleneck in medical AI by enabling the ethical use of sensitive clinical data, potentially accelerating breakthroughs in healthcare.
The ability to generate high-quality, privacy-preserving synthetic clinical data reduces previous barriers for AI development, fostering innovation without compromising patient privacy.
- · Medical AI developers
- · Healthcare institutions
- · Generative AI companies
- · Patients (indirectly through better AI)
- · Traditional data sharing models
- · Criminals exploiting medical data
Researchers gain access to more diverse and high-quality clinical datasets for AI model training and validation.
Faster development and deployment of medical AI applications lead to improved diagnostics, treatment personalization, and drug discovery.
The widespread adoption of synthetic clinical data generation standards could reshape data governance and sharing practices across highly regulated industries.
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Read at arXiv cs.AI