LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale

By Ryan M. Rogers, Subbu Subramaniam, Sean Peng, David Durfee, Seunghyun Lee, Santosh Kumar Kancha, Shraddha Sahay, Parvez Ahammad. 2020

101 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Marketing, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Sociology and Political Science

Citations in the corpus

Privacy Amplification via Random Check-Ins (2020, methodology): “For this reason, DP has been adopted by companies including Google [20, 10, 18], Apple [2], Microsoft [13], and LinkedIn [31], as well as the US Census Bureau [26].”