  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.
