论文标题
报告：在医疗环境下保存机器学习的最清晰解决方案
Report: State of the Art Solutions for Privacy Preserving Machine Learning in the Medical Context
论文作者
论文摘要
大数据上的机器学习在各个领域都越来越关注。即使如此，保护隐私的技术变得更加重要，甚至由于法律法规（例如一般数据保护法规（GDPR）），甚至必要。另一方面，数据通常在各方之间分布。特别是在医学环境中，有几个数据持有人，例如医院，我们需要处理高度敏感的价值。现实世界的情况将是现在在许多国家 /地区可用的电子患者记录中持有的数据。医疗数据已加密。用户（例如，医生，医院）只能在患者授权后解密数据。关于这种情况的主要问题之一是，是否可以为研究目的处理数据而不违反数据所有者的隐私。我们想评估可以将哪种密码机制 - 同态加密，多方计算或受信任的执行环境 - 可用于此任务。
Machine Learning on Big Data gets more and more attention in various fields. Even so privacy-preserving techniques become more important, even necessary due to legal regulations such as the General Data Protection Regulation (GDPR). On the other hand data is often distributed among various parties. Especially in the medical context there are several data holders, e.g. hospitals and we need to deal with highly sensitive values. A real world scenario would be data that is held in an electronic patient record that is available in many countries by now. The medical data is encrypted. Users (e.g. physicians, hospitals) can only decrypt the data after patient authorization. One of the main questions concerning this scenario is whether it is possible to process the data for research purposes without violating the privacy of the data owner. We want to evaluate which cryptographic mechanism - homomorphic encryption, multiparty computation or trusted execution environements - can be used for this task.
