Abstract
Generative Adversarial Networks (GANs) are an important tool to generate synthetic medical data, in order to combat the limited and difficult access to the real data sets and accelerate the innovation in the healthcare domain. Despite their promising capability, they are vulnerable to various privacy attacks that might reveal information of individuals from the training data. Preserving privacy while keeping the quality of the generated data still remains a challenging problem. We propose DP-CTGAN, which incorporates differential privacy into a conditional tabular generative model. Our experiments demonstrate that our model outperforms existing state-of-the-art models under the same privacy budget on several benchmark data sets. In addition, we combine our method with federated learning, enabling a more secure way of synthetic data generation without the need of uploading locally collected data to a central repository.
M. L. Fang and D. S. Dhami—Equal contribution.
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Notes
- 1.
We consider only tabular medical data set generation.
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Acknowledgements
The authors thank the anonymous reviewers for their valuable feedback. This work was supported by the ICT-48 Network of AI Research Excellence Center “TAILOR” (EU Horizon 2020, GA No 952215) and the Nexplore Collaboration Lab “AI in Construction” (AICO). It benefited from “safeFBDC - Financial Big Data Cluster” (FKZ:01MK21002K), funded by the BMWK as part of the GAIA-X initiative, and the HMWK cluster projects “The Third Wave of AI” and “The Adaptive Mind”.
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Fang, M.L., Dhami, D.S., Kersting, K. (2022). DP-CTGAN: Differentially Private Medical Data Generation Using CTGANs. In: Michalowski, M., Abidi, S.S.R., Abidi, S. (eds) Artificial Intelligence in Medicine. AIME 2022. Lecture Notes in Computer Science(), vol 13263. Springer, Cham. https://doi.org/10.1007/978-3-031-09342-5_17
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