Deep Generative Models - Third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings
Deep Generative Models - Third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings
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深度生成模型 - 第三届 MICCAI 研讨会，DGM4MICCAI 2023，与 MICCAI 2023 同期举行，加拿大不列颠哥伦比亚省温哥华，2023 年 10 月 8 日，会议记录
DOI：
10.1007/978-3-031-53767-7_1
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Fernandez V
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文献类型：
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作者：
Fernandez V
神经网络中的知识蒸馏是指将大型模型或数据集压缩为其自身的较小版本。我们引入了隐私蒸馏（Privacy Distillation），这是一个框架，允许生成模型教授另一个模型，而无需将其暴露给可识别的数据。在这里，我们对希望通过生成模型共享数据的数据提供者所面临的隐私问题感兴趣。立即出现的一个问题是“数据提供者如何确保生成模型不会泄露患者身份？”。我们的解决方案包括（i）在真实数据上训练第一个扩散模型； (ii) 使用该模型生成合成数据集并对其进行过滤以排除具有重新识别风险的图像； (iii)仅在过滤后的合成数据上训练第二扩散模型。我们展示了从经过隐私蒸馏训练的模型中采样的数据集可以有效降低重新识别风险，同时保持下游性能。
Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself. We introducePrivacy Distillation, a framework that allows a generative model to teach another model without exposing it to identifiable data. Here, we are interested in the privacy issue faced by a data provider who wishes to share their data via a generative model. A question that immediately arises is “How can a data provider ensure that the generative model is not leaking patient identity?”. Our solution consists of (i) training a first diffusion model on real data; (ii) generating a synthetic dataset using this model and filter it to exclude images with a re-identifiability risk; (iii) training a second diffusion model on the filtered synthetic data only. We showcase that datasets sampled from models trained with Privacy Distillation can effectively reduce re-identification risk whilst maintaining downstream performance.