# A Members First Approach to Enabling LinkedIn's Labor Market Insights at Scale

arXiv: https://arxiv.org/abs/2010.13981 · HTML: https://mlsanity.com/paper/2010.13981/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [A Consensus Privacy Metrics Framework for Synthetic Data](https://mlsanity.com/paper/2503.04980/) (2025)
- [Samplable Anonymous Aggregation for Private Federated Data Analysis](https://mlsanity.com/paper/2307.15017/) (2023)
- [Differential Privacy via Distributionally Robust Optimization](https://mlsanity.com/paper/2304.12681/) (2023)
- [On the Computational Complexity of Private High-dimensional Model Selection](https://mlsanity.com/paper/2310.07852/) (2023)
- [Nasdaq-100 Companies' Hiring Insights: A Topic-based Classification Approach to the Labor Market](https://mlsanity.com/paper/2409.00658/) (2024)

## Abstract (arXiv)

We describe the privatization method used in reporting labor market insights from LinkedIn's Economic Graph, including the differentially private algorithms used to protect member's privacy. The reports show who are the top employers, as well as what are the top jobs and skills in a given country/region and industry. We hope this data will help governments and citizens track labor market trends during the COVID-19 pandemic while also protecting the privacy of our members.