2020 Differential Privacy Temporal Map Challenge

Visit the Challenge Website.

Visit Challenge.gov for the Official Rules

Open for submissions October 1, 2020 - May 17, 2021

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.

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. 

 View the Challenge Flyer!

Timeline for Metric Paper Contest 

Timeline for Algorithm Contest

Timeline for Open Source and Development Contest

*NOTE: NIST reserves the right to revise the dates at any time

Resources

Looking for great resources to get started? Here are a few ways to learn more about the math behind differential privacy.

• A Practical Beginners' Guide to Differential Privacy - CERIAS Security Seminar, Purdue University
• Differential Privacy: A Primer for a Non-technical Audience
• Statistically Valid Inferences from Privacy Protected Data
• Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
• Open source algorithms from the 2018 Differential Privacy Synthetic Data Challenge
• An Adaptive Approach to Real-Time Aggregate Monitoring with Differential Privacy
• A Privacy Preserving Algorithm to Release Sparse High-dimensional Histograms

Email: psprizes@nist.gov