A Members First Approach to Enabling LinkedIn's Labor Market Insights at Scale
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Oct 2020.
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Citing papers per year: 2023: 3; 2024: 1; 2025: 1.
5 papers.
- 2025A Consensus Privacy Metrics Framework for Synthetic Datacited by 8
- 2023Samplable Anonymous Aggregation for Private Federated Data Analysiscited by 6
- 2023Differential Privacy via Distributionally Robust Optimizationcited by 2
- 2023On the Computational Complexity of Private High-dimensional Model Selectioncited by 1
- 2024Nasdaq-100 Companies' Hiring Insights: A Topic-based Classification Approach to the Labor Marketcited by 0
- 2025A Consensus Privacy Metrics Framework for Synthetic Data6 Mar 2025
- 2024Nasdaq-100 Companies' Hiring Insights: A Topic-based Classification Approach to the Labor Market1 Sep 2024
- 2023On the Computational Complexity of Private High-dimensional Model Selection11 Oct 2023
- 2023Samplable Anonymous Aggregation for Private Federated Data Analysis27 Jul 2023
- 2023Differential Privacy via Distributionally Robust Optimization25 Apr 2023