NIST is crowdsourcing differential privacy techniques for public safety datasets

The National Institute of Standards and Technology (NIST) is launching the Differential Privacy Temporal Map Challenge. It’s a set of contests, with cash prizes attached, intended to crowdsource new ways of handling personally identifiable information (PII) in public safety datasets.

The problem is that although rich, detailed data is valuable for researchers and building AI models — in this case, in the areas of emergency planning and epidemiology — using it raises serious and potentially dangerous data privacy and rights issues. Even if datasets are kept under a proverbial lock and key, malicious actors can, based on just a few data points, re-infer sensitive information about people.

The solution is to de-identify the data such that it remains useful without compromising individuals’ privacy. NIST already has a clear standard for what that means. In part, it says “De-identification removes identifying information from a dataset so that individual data cannot be

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