Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging

• URL: http://arxiv.org/abs/2312.04590v1
• Date: Tue, 5 Dec 2023 12:21:30 GMT
• Title: Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging
• Authors: Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
• Abstract summary: Artificial Intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive. Differential Privacy (DP) aims to circumvent these susceptibilities by setting a quantifiable privacy budget. We show that using very large privacy budgets can render reconstruction attacks impossible, while drops in performance are negligible.
• Score: 52.578054703818125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive, for example in medical imaging. Privacy Enhancing Technologies (PETs), such as Differential Privacy (DP), aim to circumvent these susceptibilities. DP is the strongest possible protection for training models while bounding the risks of inferring the inclusion of training samples or reconstructing the original data. DP achieves this by setting a quantifiable privacy budget. Although a lower budget decreases the risk of information leakage, it typically also reduces the performance of such models. This imposes a trade-off between robust performance and stringent privacy. Additionally, the interpretation of a privacy budget remains abstract and challenging to contextualize. In this study, we contrast the performance of AI models at various privacy budgets against both, theoretical risk bounds and empirical success of reconstruction attacks. We show that using very large privacy budgets can render reconstruction attacks impossible, while drops in performance are negligible. We thus conclude that not using DP -- at all -- is negligent when applying AI models to sensitive data. We deem those results to lie a foundation for further debates on striking a balance between privacy risks and model performance.

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