mlsanityData to 13 Oct 2026

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

arXiv 2002.05839 ↗First posted 14 Feb 2020cs.CRcited by 26

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Abstract (arXiv)

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.

Key facts. Cited by 26 papers in the map (data through 13 Oct 2026). First posted on arXiv 14 Feb 2020.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 3; 2022: 6; 2023: 8; 2024: 3; 2025: 5; 2026: 1.

26 papers.

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  1. 2022Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Losscited by 60
  2. 2021Covariance-Aware Private Mean Estimation Without Private Covariance Estimationcited by 46
  3. 2021Decision Making with Differential Privacy under a Fairness Lenscited by 32
  4. 2023From Robustness to Privacy and Backcited by 27
  5. 2022Faster Privacy Accounting via Evolving Discretizationcited by 14
  6. 2023Mean estimation in the add-remove model of differential privacycited by 9
  7. 2023Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterationscited by 8
  8. 2024A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theoremcited by 8
  9. 2021A Central Limit Theorem for Differentially Private Query Answeringcited by 7
  10. 2022Network Shuffling: Privacy Amplification via Random Walkscited by 6
  11. 2023Samplable Anonymous Aggregation for Private Federated Data Analysiscited by 6
  12. 2024Dimension-free Private Mean Estimation for Anisotropic Distributionscited by 6
14 more
  1. 2023Multi-Task Differential Privacy Under Distribution Skewcited by 3
  2. 2023You Don't Need Robust Machine Learning to Manage Adversarial Attack Riskscited by 3
  3. 2024Differentially Private Fair Binary Classificationscited by 3
  4. 2022Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?cited by 2
  5. 2025Differentially Private Wasserstein Barycenterscited by 2
  6. 2025Optimal Variance and Covariance Estimation under Differential Privacy in the Add-Remove Model and Beyondcited by 1
  7. 2022Differential Privacy: What is all the noise about?cited by 0
  8. 2022Anonymized Histograms in Intermediate Privacy Modelscited by 0
  9. 2023Coincidental Generationcited by 0
  10. 2023Differentially Private Distributed Estimation and Learningcited by 0
  11. 2025Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamicscited by 0
  12. 2025On the Inherent Privacy of Zeroth Order Projected Gradient Descentcited by 0
  13. 2025Privacy-Preserving Generative Modeling and Clinical Validation of Longitudinal Health Records for Chronic Diseasecited by 0
  14. 2026Predictability as a Fine-Grained Measure for Privacycited by 0