LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale
This addresses privacy concerns for LinkedIn users and enables secure marketing analytics, but it is incremental as it applies existing differential privacy research to a specific domain.
The paper tackles the problem of protecting LinkedIn members' data while providing audience engagement insights for marketing analytics by implementing a privacy system using differential privacy with user-level guarantees and a budget management service, resulting in a scalable system integrated with real-time platforms like Pinot.
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.