No funding for uncomfortable results

In 1997 Latanya Sweeney dramatically demonstrated that supposedly anonymized data was not anonymous. The state of Massachusetts had released data on 135,000 state employees and their families with obvious identifiers removed. However, the data contained zip code, birth date, and sex for each individual. Sweeney was able to cross reference this data with publicly available voter registration data to find the medical records of then Massachusetts governor William Weld.

An estimated 87% of Americans can be identified by the combination of zip code, birth date, and sex. A back-of-the-envelope calculation shows that this should not be surprising, but Sweeney appears to be the first to do this calculation and pursue the results. (Update: See such a calculation in the next post.)

In her paper Only You, Your Doctor, and Many Others May Know Sweeney says that her research was unwelcome. Over 20 journals turned down her paper on the Weld study, and nobody wanted to fund privacy research that might reach uncomfortable conclusions.

A decade ago, funding sources refused to fund re-identification experiments unless there was a promise that results would likely show that no risk existed or that all problems could be solved by some promising new theoretical technology under development. Financial resources were unavailable to support rigorous scientific studies otherwise.

There's a perennial debate over whether it is best to make security and privacy flaws public or to suppress them. The consensus, as much as there is a consensus, is that one should reveal flaws discreetly at first and then err on the side of openness. For example, a security researcher finding a vulnerability in Windows would notify Microsoft first and give the company a chance to fix the problem before announcing the vulnerability publicly. In Sweeney's case, however, there was no single responsible party who could quietly fix the world's privacy vulnerabilities. Calling attention to the problem was the only way to make things better.

• k-anonymity and t-closeness
• What is differential privacy?

Photo of Latanya Sweeney via Parker Higgins [CC BY 4.0], from Wikimedia Commons