Differential Privacy Temporal Map Challenge: Sprint 1 (Prescreened Arena)

CALLING PRESCREENED PARTICIPANTS! Help public safety agencies share data while protecting privacy. If you haven't been prescreened yet, head on over to the Open Arena to learn more and get started. #privacy

$29,000 in prizes
jan 2021
26 joined

About the project

What is Differential Privacy?

An introduction to Differential Privacy (Knexus Research).

Project background

Enormous gains in cyber and data infrastructure have led to the routine collection of massive data sets that have substantial value to diverse stakeholders in commercial, government, public health, research, and other sectors. Many large data sets collected by governments are subject to public scrutiny through open government laws. Yet, governments are also demanding greater protections on individual privacy. Further, the public itself is making demands on data collectors and distributors to protect the privacy of individual records.

Differential privacy is a standard that protects privacy no matter what third-party data is available. It does so by strictly limiting what is possible to learn about any individual in the data set. For example, differentially private algorithms are used by some government agencies to publish demographic information or other statistical aggregates while ensuring confidentiality of survey responses, and by companies to collect information about user behavior while controlling what is visible even to internal analysts.

Previous NIST PSCR differential privacy projects (NIST Differential Privacy Synthetic Data Challenge (DeID1) and The Unlinkable Data Challenge: Advancing Methods in Differential Privacy) demonstrated that crowdsourced challenges can make meaningful advancements in this difficult and complex field. Those previous contests raised awareness of the problem, brought in innovators from outside the privacy community, and demonstrated the value of head-to-head competitions for driving progress in data privacy. This Differential Privacy Temporal Map Challenge hopes to build on these results by extending the reach and utility of differential privacy algorithms to new data types.

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 invites solvers to develop algorithms that preserve data utility while guaranteeing privacy.


The Public Safety Communications Research (PSCR) Division is the primary federal laboratory conducting research, development, testing, and evaluation for public safety communications technologies. It is housed within the Communications Technology Laboratory (CTL) at the National Institute of Standards and Technology (NIST). It addresses the research and development necessary for critical features identified by public safety entities beyond the current generation of broadband technology.

The mission of PSCR Open Innovation is to create a framework in which we can work with individuals, companies, organizations, and academic institutes in rapid, more collaborative ways than traditional engagements. PSCR's Open Innovation program focuses on advancing public safety communications by leveraging the creativity, expertise, and innovative solutions from a diverse array of contributors and collaborators across the globe through financial awards and incentive-based activities.

Additional resources

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