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

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.

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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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.

The question of fairness: our proposal for improved DP algorithms in order to strengthen equity

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