Enabling PSO-Secure Synthetic Data Sharing Using Diversity-Aware Diffusion Models
This work addresses privacy-preserving data sharing in medical imaging by providing a method that balances legal compliance and utility, though it is incremental in improving synthetic data performance.
The paper tackles the dual challenge of legal privacy compliance and performance in synthetic medical data sharing by proposing a diversity-aware diffusion model framework that achieves synthetic datasets with performance within one percentage point of real-data models while ensuring predicate singling-out (PSO) security.
Synthetic data has recently reached a level of visual fidelity that makes it nearly indistinguishable from real data, offering great promise for privacy-preserving data sharing in medical imaging. However, fully synthetic datasets still suffer from significant limitations: First and foremost, the legal aspect of sharing synthetic data is often neglected and data regulations, such as the GDPR, are largley ignored. Secondly, synthetic models fall short of matching the performance of real data, even for in-domain downstream applications. Recent methods for image generation have focused on maximising image diversity instead of fidelity solely to improve the mode coverage and therefore the downstream performance of synthetic data. In this work, we shift perspective and highlight how maximizing diversity can also be interpreted as protecting natural persons from being singled out, which leads to predicate singling-out (PSO) secure synthetic datasets. Specifically, we propose a generalisable framework for training diffusion models on personal data which leads to unpersonal synthetic datasets achieving performance within one percentage point of real-data models while significantly outperforming state-of-the-art methods that do not ensure privacy. Our code is available at https://github.com/MischaD/Trichotomy.