  We present a privacy system that leverages differential privacy to protect
LinkedIn members' data while also providing audience engagement insights to
enable marketing analytics related applications. We detail the differentially
private algorithms and other privacy safeguards used to provide results that
can be used with existing real-time data analytics platforms, specifically with
the open sourced Pinot system. Our privacy system provides user-level privacy
guarantees. As part of our privacy system, we include a budget management
service that enforces a strict differential privacy budget on the returned
results to the analyst. This budget management service brings together the
latest research in differential privacy into a product to maintain utility
given a fixed differential privacy budget.
