A study of the mathematical concept of differential privacy (DP) by Columbia University computer scientists concluded the U.S. Census Bureau's move to DP as a de-identification mechanism for the 2020 Census was appropriate.
DP maintains the privacy of an individual's personal data by injecting random changes into the data.
There had been concerns that such "noise" would result in artificial deflation of reported minority populations, and a subsequent loss in funding.
The researchers found DP offers a stronger privacy guarantee, while swapping puts a disproportionate privacy burden on minority groups.
Even when implemented with sufficient privacy, the researchers found swapping generally less accurate than DP.
From Columbia Engineering News
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