New Methods for Data Mapping Needed
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Temporal map data – time and date information for geographic locations – tracking a person’s location over a period of time, is particularly helpful to public safety agencies. Large public safety datasets containing health and location information could help epidemiologists identify the source and spread of a specific infection and help public safety personnel with emergency response in the event of a large-scale disaster.
However, those datasets are made up of individual records containing personally identifiable information. A call to 911 for example, starts with an individual’s name, address and the time of the call and eventually may eventually include information on a person’s medical diagnosis and prescriptions. Even if an individual’s data is anonymized, public safety records containing personal data, time and location data can be linked to third-party databases and used to re-identify individuals.
The US National Institute of Standards and Technology is therefore asking the public to help it find new ways to de-identify public safety datasets so researchers and policy makers can glean insights while protecting individual privacy.
With fully de-identified datasets containing PII (personally identifiable information), researchers can ensure data remains useful while limiting what can be learned about any individual, regardless of what third-party information is available.
Differential privacy is considered a viable method to protect PII while still retaining enough details in the data to make it useful. It adds noise or distortions to some of the data so that general conclusions can be drawn from analysis, but when a statistic is released, information about an individual is not revealed.
Launched Oct. 1, NIST’s Differential Privacy Temporal Map Challenge features three contests in which participants apply differential privacy methods to time-stamped map data, where one individual in the data may contribute to a sequence of events. The goal is to create a privacy-preserving dashboard map that shows changes across different map segments over time.
According to gcn.com, NIST is also asking the public to help develop metrics and scoring methods to evaluate the accuracy of the algorithms that de-identify the temporal map data.
