对抗性数据加密
Adversarial Data Encryption
摘要：
在大数据时代，很多组织都面临着数据共享的困境。定期的数据共享对于以人为中心的讨论和交流通常是必要的，特别是在医疗场景中。然而，不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，这样对于人类来说，加密的数据看起来与原始版本相同，但对于机器学习方法来说，它们是误导性的。为了证明我们的方法的有效性，我们与北京天坛医院合作，该医院拥有世界领先的神经中心。我们邀请$3$医生根据真实世界的医学图像手动检查我们的加密方法。结果表明，加密后的图像可以用于医生的诊断，但不能用于机器学习方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
