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

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Ryan Rogers
Subbu Subramaniam
Sean Peng
David Durfee
Seunghyun Lee
Santosh Kumar Kancha
Shraddha Sahay
Parvez Ahammad


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.

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How to Cite
Rogers, Ryan, Subbu Subramaniam, Sean Peng, David Durfee, Seunghyun Lee, Santosh Kumar Kancha, Shraddha Sahay, and Parvez Ahammad. 2021. “LinkedIn’s Audience Engagements API: A Privacy Preserving Data Analytics System at Scale”. Journal of Privacy and Confidentiality 11 (3).
TPDP 2020