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 is of particular interest to the public safety community. 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 asks participants to develop algorithms that preserve data utility as much as possible while guaranteeing individual privacy is protected. The challenge features a series of coding sprints to apply differential privacy methods to temporal map data, where one individual in the data may contribute to a sequence of events. The goal is to create a privacy-preserving dashboard map that shows changes across different map segments over time.
Partner Organization: National Institute of Standards and Technology
Awards: $276,000 in total prizes
Open Date: October 1, 2020
Close Date: May 17, 2021
Frequency: Once
For more information, visit: https://www.drivendata.org/competitions/68/competition-differential-privacy-maps-1/