Using differential privacy to safely release earnings data on post-secondary grads

When the U.S. Census Bureau needed to release earnings data post-secondary graduates, Tumult Labs Chief Scientist and co-founder Ashwin Machanavajjhala helped the agency design a new privacy-safe algorithm.

Research

Summary:

When the U.S. Census Bureau needed to release data on the median earnings of post-secondary graduates, one Tumult Lab founder helped the Census Bureau design a new differentially private algorithm. By constructing a histogram of earnings and adding geometric noise to the original data, the team created a better algorithm that yields more accurate results.

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