US bans differential privacy in Census data
Last week , the United States Department of Commerce issued an order declaring that "noise infusion" will be banned from all statistical products published by the Census Bureau and the Bureau of Economic Analysis.
What does it mean, and why should you care?
Statistical products are a bunch of numbers published from a secret dataset. Often, that dataset contains confidential information, and it is important that the numbers don't reveal that information. The U.S. Census is a well-known example: the statistics are made public, but the contents of each form filled by individual U.S. residents must stay secret.
Scientists have developed a number of techniques that can be used to publish useful statistics while protecting the privacy of the original data. This field is called disclosure avoidance in statistical communities. Here are a few of these techniques.
Some of these techniques, when combined, achieve a definition called differential privacy . This definition has a lot of nice fundamental properties and is widely considered the gold standard of privacy protection among scientists. To achieve it, scientists typically rely on a combination of contribution bounding and carefully-calibrated noise addition.
From 1990 to 2010, the U.S. Census Bureau primarily relied on swapping for the decennial census. Then, they realized that this technique was actually very unsafe , and that it was pretty easy to reconstruct individual records using the published statistics. This is bad, because the Bureau is required by federal law to keep these records confidential. So they tried a few alternative approaches, and decided to adopt differential privacy for the 2020 Census: this was the one that kept the statistics most useful, while preventing these attacks.