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Tunable Privacy Risk Evaluation of Generative Adversarial Networks
Bayrem Kaabachi1, Farah Briki1, Bogdan Kulynych1
1Biomedical Data Science Center, Lausanne University Hospital (CHUV) and University of Lausanne, Switzerland.
Studies in Health Technology and Informatics
|August 23, 2024
Summary
Generative Adversarial Networks (GANs) can leak private training data. This study introduces a new privacy risk evaluation technique for GANs, improving upon existing membership inference attacks.
Area of Science:
- Machine Learning
- Artificial Intelligence
- Data Privacy
Background:
- Generative Adversarial Networks (GANs) excel at creating realistic synthetic data.
- However, GANs and their outputs can inadvertently expose sensitive information from their training datasets.
- Evaluating privacy risks is crucial before deploying GANs.
Purpose of the Study:
- To develop a novel, practical technique for assessing privacy risks in GANs.
- To overcome limitations of current membership inference attacks (MIAs), such as strong assumptions and high computational costs.
- To provide a more comprehensive privacy risk evaluation for GANs.
Main Methods:
- Exploiting the discriminator outputs within the standard GAN architecture.
- Simulating membership inference attacks (MIAs) to estimate privacy leakage.
- Evaluating the technique on synthetic image generation in radiology and ophthalmology.
Main Results:
- The proposed technique offers a more complete understanding of privacy threats.
- It enables worst-case privacy risk estimation.
- Achieves higher precision in identifying privacy attacks compared to existing methods.
Conclusions:
- The novel technique effectively evaluates privacy risks in GANs.
- It presents a more practical and precise alternative to current MIA methods.
- Enhances the secure application of synthetic data generation in sensitive domains.
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