Beat Future Infectious Diseases by Crowdsourcing Strategic Data

By Rich Murr, CIO, Epicor Software

To blunt the impact of COVID-19, a vast number of medical, industrial, and financial resources are being deployed. And while these efforts are important, we should also look ahead so that we are better able to prevent future pandemics by crowdsourcing strategic, available data that would offer a bigger picture.

As someone who previously served in the United States Marines and who now serves as a CIO for a software company, I can emphatically say that regardless of your line of work, one of the best first lines of defense is information – specifically, quality information that can be speedily obtained and assessed. In the case of fighting infectious diseases, this information is critical for our epidemiologists. They need information so that we can combat potential threats before they become widespread.

It’s clear that relying solely on closed societies, public health institutions, or the

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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, that’s 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 for building AI models — in this case, in the areas of emergency planning and epidemiology — it raises serious and potentially dangerous data privacy and rights issues. Even if datasets are kept under 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, and simply put, it says that “De-identification removes identifying information from a dataset so that

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