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

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.

References in corpus (6)

• Deep Learning with Differential Privacy
• Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.1)
• PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn
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Cited by in corpus (11)

• Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates
• Random Sampling Plus Fake Data: Multidimensional Frequency Estimates With Local Differential Privacy
• Network Shuffling: Privacy Amplification via Random Walks
• Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
• HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data
• U.S. Broadband Coverage Data Set: A Differentially Private Data Release
• A Central Limit Theorem for Differentially Private Query Answering
• Decision Making with Differential Privacy under a Fairness Lens
• A Members First Approach to Enabling LinkedIn's Labor Market Insights at Scale
• Differentially Private Histograms under Continual Observation: Streaming Selection into the Unknown
• Optimum Noise Mechanism for Differentially Private Queries in Discrete Finite Sets