The NIST, PSCR Differential Privacy Temporal Map Challenge ran from October 2020 through June 2021 awarding $129,000 in cash prizes. The goal of the challenge was to seek innovative algorithms to de-identify public safety-related data with a privacy guarantee. The challenge also sought novel methods of evaluating the quality of synthetic data.
You can try out your own solution using SDNist, an open source Python implementation of our data and scoring metrics.
The challenge was highly successful with more than 70 unique algorithms submissions across all three sprints of the challenge. Four of those algorithms have been open sourced (links in winners table below). Three solutions participated in the Development Contest, where teams were coached by NIST experts to improve the robustness and documentation of their code, creating easy-to-use implementations of sophisticated differential privacy algorithms.
The challenge was implemented by DrivenData with assistance from HeroX. Christine Task from Knexus Research Corporation served as the program’s technical lead. Gary Howarth served as the prize manager.
|Team||Total Awards||Open Sourced||Development Contest||APA Citation|
|Minutemen||$48,000||Yes||Repository link||McKenna R. (2021). Adaptive Granularity Mechanism (version 1.0). URL: https://github.com/ryan112358/nist-synthetic-data-2021|
|DPSyn||$38,000||Yes||Repository link||Chen A., Li N., Li Z., Wang T. (2021). DPSyn: An algorithm for synthesizing microdata for data analysis while satisfying differential privacy (version 1.0). URL: https://github.com/agl-c/deid2_dpsyn|
|jimking100||$24,000||Yes||Repository link||King, J. (2021). Privitized Histograms (Version 1.0.0) [Computer software]. https://github.com/JimKing100/PrivacyHistos|
|Duke Privacy Team||$12,000||--||--|
|MGD: A Utility Metric for Private Data Publication||1st||$5,000|
|Practical DP Metric||2nd||$3,000|
|Confusion Matrix Metric||2nd||$3,000|
|Bounding Utility Loss via Classifiers||3rd||$2,000|
|Confusion Matrix Metric||People's Choice Award||$1,000|
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 public safety agencies 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 invited solvers to develop algorithms and metrics that preserve data utility while guaranteeing individual privacy is protected.
Participants competed in a series of coding sprints using differential privacy methods on temporal map data. The goal was to create a privacy-preserving dashboard map that shows changes across different map segments over time.
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.
To find out more about the challenge and winners, visit Challenge.gov.