Security News > 2020 > October > NIST crowdsourcing challenge aims to de-identify public data sets to protect individual privacy

NIST has launched a crowdsourcing challenge to spur new methods to ensure that important public safety data sets can be de-identified to protect individual privacy.

The Differential Privacy Temporal Map Challenge includes a series of contests that will award a total of up to $276,000 for differential privacy solutions for complex data sets that include information on both time and location.

For critical applications such as emergency planning and epidemiology, public safety responders may need access to sensitive data, but sharing that data with external analysts can compromise individual privacy.

By fully de-identifying data sets containing PII, researchers can ensure data remains useful while limiting what can be learned about any individual in the data regardless of what third-party information is available.

The three Temporal Map Algorithms sprints will award a total prize purse of $147,000 over a series of three sprints to develop algorithms that preserve data utility of temporal and spatial map data sets while guaranteeing privacy.

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