Jeffrey Mervis at Science: “The U.S. Census Bureau is making waves among social scientists with what it calls a “sea change” in how it plans to safeguard the confidentiality of data it releases from the decennial census.
The agency announced in September 2018 that it will apply a mathematical concept called differential privacy to its release of 2020 census data after conducting experiments that suggest current approaches can’t assure confidentiality. But critics of the new policy believe the Census Bureau is moving too quickly to fix a system that isn’t broken. They also fear the changes will degrade the quality of the information used by thousands of researchers, businesses, and government agencies.
The move has implications that extend far beyond the research community. Proponents of differential privacy say a fierce, ongoing legal battle over plans to add a citizenship question to the 2020 census has only underscored the need to assure people that the government will protect their privacy....
Differential privacy, first described in 2006, isn’t a substitute for swapping and other ways to perturb the data. Rather, it allows someone—in this case, the Census Bureau—to measure the likelihood that enough information will “leak” from a public data set to open the door to reconstruction.
“Any time you release a statistic, you’re leaking something,” explains Jerry Reiter, a professor of statistics at Duke University in Durham, North Carolina, who has worked on differential privacy as a consultant with the Census Bureau. “The only way to absolutely ensure confidentiality is to release no data. So the question is, how much risk is OK? Differential privacy allows you to put a boundary” on that risk....
In the case of census data, however, the agency has already decided what information it will release, and the number of queries is unlimited. So its challenge is to calculate how much the data must be perturbed to prevent reconstruction....
A professor of labor economics at Cornell University, Abowd first learned that traditional procedures to limit disclosure were vulnerable—and that algorithms existed to quantify the risk—at a 2005 conference on privacy attended mainly by cryptographers and computer scientists. “We were speaking different languages, and there was no Rosetta Stone,” he says.
He took on the challenge of finding common ground. In 2008, building on a long relationship with the Census Bureau, he and a team at Cornell created the first application of differential privacy to a census product. It is a web-based tool, called OnTheMap, that shows where people work and live….
The three-step process required substantial computing power. First, the researchers reconstructed records for individuals—say, a 55-year-old Hispanic woman—by mining the aggregated census tables. Then, they tried to match the reconstructed individuals to even more detailed census block records (that still lacked names or addresses); they found “putative matches” about half the time.
Finally, they compared the putative matches to commercially available credit databases in hopes of attaching a name to a particular record. Even if they could, however, the team didn’t know whether they had actually found the right person.
Abowd won’t say what proportion of the putative matches appeared to be correct. (He says a forthcoming paper will contain the ratio, which he calls “the amount of uncertainty an attacker would have once they claim to have reidentified a person from the public data.”) Although one of Abowd’s recent papers notes that “the risk of re-identification is small,” he believes the experiment proved reidentification “can be done.” And that, he says, “is a strong motivation for moving to differential privacy.”…
Such arguments haven’t convinced Ruggles and other social scientists opposed to applying differential privacy on the 2020 census. They are circulating manuscripts that question the significance of the census reconstruction exercise and that call on the agency to delay and change its plan....
Ruggles, meanwhile, has spent a lot of time thinking about the kinds of problems differential privacy might create. His Minnesota institute, for instance, disseminates data from the Census Bureau and 105 other national statistical agencies to 176,000 users. And he fears differential privacy will put a serious crimp in that flow of information…
There are also questions of capacity and accessibility. The centers require users to do all their work onsite, so researchers would have to travel, and the centers offer fewer than 300 workstations in total....
Abowd has said, “The deployment of differential privacy within the Census Bureau marks a sea change for the way that official statistics are produced and published.” And Ruggles agrees. But he says the agency hasn’t done enough to equip researchers with the maps and tools needed to navigate the uncharted waters….(More)”.
