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2020 Differential Privacy Temporal Map Challenge

Graphic with text that reads: Differential Privacy Temporal Map Challenge

Visit the Challenge Website and Sign up for challenge updates here.

Apply differential privacy over space and time

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.

Potential elements of the challenge

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.

  • Temporal map data: The challenge will feature public safety data sets with geographic and temporal elements. Solutions will seek to satisfy differential privacy while preserving characteristics of original data sets as much as possible, including sequential data and geographic characteristics.
  • Utility metrics: Participants may submit white papers on new and innovative ways of measuring the accuracy of data sets produced by differential privacy algorithms.
  • De-identification algorithms: Participants build and submit algorithms to preserve data accuracy while guaranteeing privacy on temporal map data sets.
  • Open source development: Beyond expanding the types of data that can be made differentially private, this challenge seeks to advance and disseminate the resulting software. 

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! 


Created September 1, 2020