# PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn

arXiv: https://arxiv.org/abs/1809.07754 · HTML: https://mlsanity.com/paper/1809.07754/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [Faster Privacy Accounting via Evolving Discretization](https://mlsanity.com/paper/2207.04381/) (2022)
- [Distribution-Invariant Differential Privacy](https://mlsanity.com/paper/2111.05791/) (2021)
- [QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems](https://mlsanity.com/paper/2211.05249/) (2022)
- [Anonymized Histograms in Intermediate Privacy Models](https://mlsanity.com/paper/2210.15178/) (2022)
- [M2M: A general method to perform various data analysis tasks from a differentially private sketch](https://mlsanity.com/paper/2211.14062/) (2022)
- [Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor](https://mlsanity.com/paper/2506.01162/) (2025)
- [Blind Targeting: Personalization under Third-Party Privacy Constraints](https://mlsanity.com/paper/2507.05175/) (2025)

## Abstract (arXiv)

Preserving privacy of users is a key requirement of web-scale analytics and reporting applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. We focus on the problem of computing robust, reliable analytics in a privacy-preserving manner, while satisfying product requirements. We present PriPeARL, a framework for privacy-preserving analytics and reporting, inspired by differential privacy. We describe the overall design and architecture, and the key modeling components, focusing on the unique challenges associated with privacy, coverage, utility, and consistency. We perform an experimental study in the context of ads analytics and reporting at LinkedIn, thereby demonstrating the tradeoffs between privacy and utility needs, and the applicability of privacy-preserving mechanisms to real-world data. We also highlight the lessons learned from the production deployment of our system at LinkedIn.