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

by   Ryan Rogers, et al.

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.


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

We present a privacy system that leverages differential privacy to prote...

Differentially Private Call Auctions and Market Impact

We propose and analyze differentially private (DP) mechanisms for call a...

Benchmarking Differentially Private Residual Networks for Medical Imagery

Hospitals and other medical institutions often have vast amounts of medi...

Revealing Network Structure, Confidentially: Improved Rates for Node-Private Graphon Estimation

Motivated by growing concerns over ensuring privacy on social networks, ...

Statistics cannot prove that the Huanan Seafood Wholesale Market was the early epicenter of the COVID-19 pandemic

We criticize a statistical proof of the hypothesis that the Huanan seafo...

Data Cooperatives: Towards a Foundation for Decentralized Personal Data Management

Data cooperatives with fiduciary obligations to members provide a promis...

Between the Information Economy and Student Recruitment: Present Conjuncture and Future Prospects

In university programs and curricula, in general we react to the need to...