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

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arxiv 2002.05839 v3 pith:AHJLESPE submitted 2020-02-14 cs.CR

Ryan Rogers , Subbu Subramaniam , Sean Peng , David Durfee , Seunghyun Lee , Santosh Kumar Kancha , Shraddha Sahay , Parvez Ahammad This is my paper · ORCID
classification cs.CR
keywords privacysystembudgetdifferentialanalyticsdataaudiencelinkedin
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

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  1. Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics

    cs.LG 2025-06 reject novelty 5.0 of 10

    A differentially private particle-gradient algorithm generates continuous-time synthetic trajectories from one snapshot per person, but its headline recovery rate applies only to a non-private infinite-particle idealization.

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