Differential Privacy Temporal Map Challenge

Help public safety agencies share data while protecting privacy. This is a series of contests to develop algorithms that preserve the usefulness of temporal map data while guaranteeing individual privacy is protected. #privacy

$147,000 in prizes
jun 2021
281 joined

Differential Privacy Temporal Map Challenge


The Challenge

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 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 required participants to develop algorithms that preserve data utility as much as possible while guaranteeing individual privacy is protected. The challenge featured a series of coding sprints to apply differential privacy methods to temporal map data, where each record is tied to a location and each individual may contribute to a sequence of events.

The Results

When the NIST synthetic data challenges started in 2018, there was skepticism as to whether synthetic data was feasible with differential privacy. Over the course of the 6 sprints across two challenges, we have seen the competitors rise to the occasion, discover unexpected powerful tricks and techniques, and address problems such as large, complex feature spaces, sparse data, high sensitivity queries (temporal data), heterogeneous map segments, small epsilon values, edit constraints and all the complexities of real world data.

They were able not only to perform well on these real world problems. Top contestants of the final sprint also demonstrated algorithms that produce records with both more privacy and greater accuracy than the typical subsampling techniques used by many government agencies to release records. These results hold immediate promise for public safety communities and other groups interested in data sharing with the formal privacy guarantees offered by differential privacy.

Check out the challenge sprints below and learn more about the winners and their leaderboard-topping approaches!


SPRINT 1 (BALTIMORE 911 INCIDENTS): RESULTS AND WINNERS

SPRINT 2 (AMERICAN COMMUNITY SURVEY): RESULTS AND WINNERS

SPRINT 3 (CHICAGO TAXI RIDES): RESULTS AND WINNERS

CHALLENGE REPOSITORY AND DATA SETS


Sprint 1

privacy

Open Arena

START HERE! Help public safety agencies share data while protecting privacy. This is part of a series of contests to develop algorithms that preserve the usefulness of temporal map data while guaranteeing individual privacy is protected. #privacy

169 joined
nov 2020
competition has ended
PRE-APPROVAL NEEDED

privacy

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

26 joined
$29,000 in prizes
jan 2021
competition has ended
$29,000

Sprint 2

privacy

Open Arena

START HERE! Help public safety agencies share data while protecting privacy. This is part of a series of contests to develop algorithms that preserve the usefulness of temporal map data while guaranteeing individual privacy is protected. #privacy

76 joined
feb 2021
competition has ended
PRE-APPROVAL NEEDED

privacy

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

24 joined
$39,000 in prizes
mar 2021
competition has ended
$39,000

Sprint 3

privacy

Open Arena

START HERE! Help public safety agencies share data while protecting privacy. This is part of a series of contests to develop algorithms that preserve the usefulness of temporal map data while guaranteeing individual privacy is protected. #privacy

89 joined
may 2021
competition has ended
PRE-APPROVAL NEEDED

privacy

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

22 joined
$79,000 in prizes
jun 2021
competition has ended
$79,000