The scientific method presents a key challenge to privacy because it requires many samples to support a claim. When samples are commercially valuable or privacy-sensitive enough, their owners have strong reasons to avoid releasing them for scientific study. Privacy techniques seek to mitigate this tension by enforcing limits on one's ability to use studied samples for secondary purposes. Recent work has begun combining these techniques into end-to-end systems for protecting data. In this work, we assemble the first such combination which is sufficient for a privacy-layman to use familiar tools to experiment over private data while the infrastructure automatically prohibits privacy leakage. We support this theoretical system with a prototype within the Syft privacy platform using the PyTorch framework.

翻译：科学方法对隐私提出了关键的挑战,因为它需要许多样本来证明索赔要求。当样本具有商业价值或对隐私足够敏感时,其所有者有充分的理由避免将其释放用于科学研究。隐私技术试图通过限制个人将研究过的样本用于次要目的的能力来缓解这种紧张。最近的工作已经开始将这些技术纳入数据保护端对端系统。在这项工作中,我们汇集了第一个这样的组合,使隐私维护者能够使用熟悉的工具对私人数据进行实验,而基础设施则自动禁止隐私渗漏。我们支持这个理论系统,在Syft隐私平台内使用PyTorch框架的原型。