Large data sets containing personally identifiable information (PII) are exceptionally valuable resources for research and policy analysis in a host of fields supporting America's First Responders such as emergency planning and epidemiology.
Temporal map data—information that is geographically situated and may change over time—is of particular interest to the public safety community in applications such as optimizing response time and personnel placement, natural disaster response, epidemic tracking, demographic data and civic planning. Yet, the ability to track a person's location over a period of time presents particularly serious privacy concerns.
The Differential Privacy Temporal Map Challenge will invite solvers to develop algorithms and metrics that preserve data utility while guaranteeing individual privacy is protected.
Participants will compete in a series of coding sprints using differential privacy methods on temporal map data. These data sets may contain the records of hundreds or thousands of individuals, each contributing to a sequence of events. The goal is to create a privacy-preserving dashboard map that shows changes across different map segments over time.
The best solutions will be publicly recognized and up to $276,000 in cash prizes may be awarded to top-performing teams.
The NIST PSCR Differential Privacy Temporal Map Challenge follows on the success of the 2018 Differential Privacy Synthetic Data Challenge, extending the reach and utility of differential privacy algorithms.
We can't wait to see what you come up with! Sign up above to get notified when the challenge is released!
Looking for great resources to get started? Here are a few ways to learn more about the math behind differential privacy.
Good luck and stay tuned for more information coming this fall!