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

arXiv: https://arxiv.org/abs/2002.05839 · HTML: https://mlsanity.com/paper/2002.05839/

## Key facts

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

## Built on by (most cited first, 26 of 26)

- [Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss](https://mlsanity.com/paper/2205.13710/) (2022)
- [Covariance-Aware Private Mean Estimation Without Private Covariance Estimation](https://mlsanity.com/paper/2106.13329/) (2021)
- [Decision Making with Differential Privacy under a Fairness Lens](https://mlsanity.com/paper/2105.07513/) (2021)
- [From Robustness to Privacy and Back](https://mlsanity.com/paper/2302.01855/) (2023)
- [Faster Privacy Accounting via Evolving Discretization](https://mlsanity.com/paper/2207.04381/) (2022)
- [Mean estimation in the add-remove model of differential privacy](https://mlsanity.com/paper/2312.06658/) (2023)
- [Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations](https://mlsanity.com/paper/2310.19978/) (2023)
- [A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem](https://mlsanity.com/paper/2409.09558/) (2024)
- [A Central Limit Theorem for Differentially Private Query Answering](https://mlsanity.com/paper/2103.08721/) (2021)
- [Network Shuffling: Privacy Amplification via Random Walks](https://mlsanity.com/paper/2204.03919/) (2022)
- [Samplable Anonymous Aggregation for Private Federated Data Analysis](https://mlsanity.com/paper/2307.15017/) (2023)
- [Dimension-free Private Mean Estimation for Anisotropic Distributions](https://mlsanity.com/paper/2411.00775/) (2024)
- [Multi-Task Differential Privacy Under Distribution Skew](https://mlsanity.com/paper/2302.07975/) (2023)
- [You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks](https://mlsanity.com/paper/2306.09951/) (2023)
- [Differentially Private Fair Binary Classifications](https://mlsanity.com/paper/2402.15603/) (2024)
- [Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?](https://mlsanity.com/paper/2205.00436/) (2022)
- [Differentially Private Wasserstein Barycenters](https://mlsanity.com/paper/2510.03021/) (2025)
- [Optimal Variance and Covariance Estimation under Differential Privacy in the Add-Remove Model and Beyond](https://mlsanity.com/paper/2509.04919/) (2025)
- [Differential Privacy: What is all the noise about?](https://mlsanity.com/paper/2205.09453/) (2022)
- [Anonymized Histograms in Intermediate Privacy Models](https://mlsanity.com/paper/2210.15178/) (2022)
- [Coincidental Generation](https://mlsanity.com/paper/2304.01108/) (2023)
- [Differentially Private Distributed Estimation and Learning](https://mlsanity.com/paper/2306.15865/) (2023)
- [Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics](https://mlsanity.com/paper/2506.12203/) (2025)
- [On the Inherent Privacy of Zeroth Order Projected Gradient Descent](https://mlsanity.com/paper/2507.05610/) (2025)
- [Privacy-Preserving Generative Modeling and Clinical Validation of Longitudinal Health Records for Chronic Disease](https://mlsanity.com/paper/2512.00434/) (2025)
- [Predictability as a Fine-Grained Measure for Privacy](https://mlsanity.com/paper/2606.20546/) (2026)

## 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.