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CHIPS Articles: Protecting privacy in big data analytics

Protecting privacy in big data analytics
Take NIST’s ‘Unlinkable Data Challenge’
By CHIPS Magazine - May 1, 2018
The National Institute of Standards and Technology (NIST) is requesting help in making the personal information in large databases safe for scientists to use — without risking the privacy of the individuals behind it.

NIST announced the Unlinkable Data Challenge which was created to help the public safety community conduct research using data gathered with personal digital devices and taken from large databases such as driver’s license and health care records. Much of this data includes personal information that can be used to identify its source. Exposing this data risks those individuals’ privacy, but the inability to share it impedes research in many fields, including thwarting crime, fighting fires and slowing the spread of epidemics, NIST said in a release.

The key to unleashing the data’s power for the public safety community lies in finding automated ways to effectively “de-identify” or de-link personal information while maintaining the data’s analytic value for research. The goal of the challenge is to create methods to assist the public safety community make better decisions while protecting the public from data leaks and cyberattacks.

NIST said the challenge will have three phases with $190,000 of total prize money split among them. The first phase asks competitors to propose an overall conceptual method of de-identifying a data set. The subsequent two phases will involve developing and refining the algorithms to implement the approach.

Submissions for the first phase close on July 26, 2018. Complete details are here.

The goal is to make personal data available for scientific research without risking individuals' privacy. Credit: K. Irvine/NIST
The goal is to make personal data available for scientific research without risking individuals' privacy. Credit: K. Irvine/NIST
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