The agency is putting up cash prizes for new differential privacy algorithms, as well as methods for testing the quality of those solutions and using open source code.
Federal, state and local agencies are collecting massive amounts of data on public safety and policing and one federal agency wants to help protect the privacy of the individuals that data represents and is offering $276,000 for ideas.
One of the best uses of data for public safety is mapping hotspots during pandemics, severe weather and other emergency or disaster situations. Combine that location data with timestamps and the information becomes even more valuable. However, maintaining the anonymity of the individuals that data represents is notoriously difficult.
“Temporal map data, with its ability to track a person’s location over a period of time, is particularly helpful to public safety agencies when preparing for disaster response, fire-fighting and law enforcement tactics,” Gary Howarth, prize challenge manager for the National Institute of Standards and Technology, said in a release announcing the new competition. “The goal of this challenge is to develop solutions that can protect the privacy of individual citizens and first responders when agencies need to share data.”
To achieve, NIST launched the Differential Privacy Temporal Map Challenge, putting up $276,000 in prize money for “privacy solutions for complex data sets that include information on both time and location,” according to the announcement.
The challenge is focused on data that has been anonymized to share with external partners, including other public safety organizations.
“Even if data is anonymized, malicious parties may be able to link the anonymized records with third-party data and re-identify individuals,” NIST officials said. “And, when data has both geographical and time information, the risk of re-identification increases significantly.”
The agency is looking specifically at differential privacy techniques—also known as formal privacy—in which the data owners inject “noise” into the datasets. Using an algorithm that makes targeted changes to the data, organizations can prevent outside actors—malicious or otherwise—from reverse engineering identities.
Differential privacy techniques are being used by the Census Bureau to mask identities for the new citizenship estimates, the administration’s workaround for not being able to include a citizenship question in the 2020 count.
“Differential privacy provides much stronger data protection than anonymity; it’s a provable mathematical guarantee that protects personally identifiable information,” NIST officials said. “By fully de-identifying data sets containing PII, we can ensure data remains useful while limiting what can be learned about any individual in the data regardless of what third-party information is available.”
The latest challenge is split into three “sprints” focused on developing better privacy algorithms for use on geographic and temporal data, with a total prize pool of $147,000. The challenge also includes two side prizes: $29,000 for developing new ways to measure the quality of privacy techniques and $100,000 for teams using and promoting open source code in their solutions.
The submission window opened on October 1 and runs through 8 p.m. May 17, 2021.
Editor's Note: This story has been updated to correct the total in cash prizes available.