论文标题
对抗数据加密
Adversarial Data Encryption
论文作者
论文摘要
在大数据时代，许多组织面临数据共享的困境。常规数据共享通常是以人为本的讨论和沟通所必需的，尤其是在医学情况下。但是，未受保护的数据共享也可能导致数据泄漏。受对抗攻击的启发，我们提出了一种数据加密的方法，因此对于人类而言，加密数据看起来与原始版本相同，但对于机器学习方法，它们具有误导性。为了显示我们方法的有效性，我们与北京Tiantan医院合作，该医院拥有世界领先的神经系统中心。我们邀请$ 3 $医生根据现实世界医学图像手动检查我们的加密方法。结果表明，加密图像可用于医生的诊断，而不是通过机器学习方法来诊断。
In the big data era, many organizations face the dilemma of data sharing. Regular data sharing is often necessary for human-centered discussion and communication, especially in medical scenarios. However, unprotected data sharing may also lead to data leakage. Inspired by adversarial attack, we propose a method for data encryption, so that for human beings the encrypted data look identical to the original version, but for machine learning methods they are misleading. To show the effectiveness of our method, we collaborate with the Beijing Tiantan Hospital, which has a world leading neurological center. We invite $3$ doctors to manually inspect our encryption method based on real world medical images. The results show that the encrypted images can be used for diagnosis by the doctors, but not by machine learning methods.
