  It is challenging to balance the privacy and accuracy for federated query
processing over multiple private data silos. In this work, we will demonstrate
an end-to-end workflow for automating an emerging privacy-preserving technique
that uses a deep learning model trained using the Differentially-Private
Stochastic Gradient Descent (DP-SGD) algorithm to replace portions of actual
data to answer a query. Our proposed novel declarative privacy-preserving
workflow allows users to specify "what private information to protect" rather
than "how to protect". Under the hood, the system automatically chooses
query-model transformation plans as well as hyper-parameters. At the same time,
the proposed workflow also allows human experts to review and tune the selected
privacy-preserving mechanism for audit/compliance, and optimization purposes.
