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

TL;DR

This paper introduces a privacy-preserving data analytics system for LinkedIn that uses differential privacy to protect user data while enabling real-time marketing insights through integration with existing analytics platforms.

Contribution

It presents a novel privacy system combining differential privacy algorithms with a budget management service tailored for large-scale, real-time audience engagement analytics.

Findings

Provides user-level privacy guarantees

Integrates differential privacy with Pinot analytics platform

Maintains utility within privacy budget constraints

Abstract

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.