Is Medical Chest X-ray Data Anonymous?

With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical data sets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data contains sensitive patient-related information and is therefore usually anonymized by removing patient identifiers, e.g., patient names before publication. To the best of our knowledge, we are the first to show that a well-trained deep learning system is able to recover the patient identity from chest X-ray data. We demonstrate this using the publicly available large-scale ChestX-ray14 dataset, a collection of 112,120 frontal-view chest X-ray images from 30,805 unique patients. Our verification system is able to identify whether two frontal chest X-ray images are from the same person with an AUC of 0.9940 and a classification accuracy of 95.55%. We further highlight that the proposed system is able to reveal the same person even ten and more years after the initial scan. When pursuing a retrieval approach, we observe an mAP@R of 0.9748 and a precision@1 of 0.9963. Based on this high identification rate, a potential attacker may leak patient-related information and additionally cross-reference images to obtain more information. Thus, there is a great risk of sensitive content falling into unauthorized hands or being disseminated against the will of the concerned patients. Especially during the COVID-19 pandemic, numerous chest X-ray datasets have been published to advance research. Therefore, such data may be vulnerable to potential attacks by deep learning-based re-identification algorithms.

随着近年来深度学习技术的发展,公开的医疗数据集称为诊断算法能够成功的关键因素之一。医疗数据包含敏感的个人信息,因此这些信息常常会被移除,例如病人的姓名。据我们所知,我们是第一个展示一个预训练的深度学习模型可以从X光数据中恢复病人的个人信息的研究小组。我们使用公认的Chest-ray14 数据集进行测试,这个数据集拥有112120前侧X光数据,由30805独立病人采集。我们的系统用可以有效识别两张X光图像是否来自同一个人,甚至两张图像的生成时间相差多年。基于这样的高识别率,一个潜在的攻击者可以泄露这些个人信息,并通过交叉对比获得更多的信息。因此,敏感信息的泄漏横在面临很高的风险。特别是对于COVID-19疫情,多个胸部X光数据集已经被公开。所以,这些数据的隐私应该被考虑进行有效保护。


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