Differential privacy for public health data: An innovative tool to optimize information sharing while protecting data confidentiality

Amalie Dyda, Michael Purcell, Stephanie Curtis, Emma Field, Priyanka Pillai, Kieran Ricardo, Haotian Weng, Jessica C. Moore, Michael Hewett, Graham Williams, Colleen L. Lau

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    Abstract

    Coronavirus disease 2019 (COVID-19) has highlighted the need for the timely collection and sharing of public health data. It is important that data sharing is balanced with protecting confidentiality. Here we discuss an innovative mechanism to protect health data, called differential privacy. Differential privacy is a mathematically rigorous definition of privacy that aims to protect against all possible adversaries. In layperson's terms, statistical noise is applied to the data so that overall patterns can be described, but data on individuals are unlikely to be extracted. One of the first use cases for health data in Australia is the development of the COVID-19 Real-Time Information System for Preparedness and Epidemic Response (CRISPER), which provides proof of concept for the use of this technology in the health sector. If successful, this will benefit future sharing of public health data.

    Original languageEnglish
    Article number100366
    Pages (from-to)1-7
    Number of pages7
    JournalPatterns
    Volume2
    Issue number12
    DOIs
    Publication statusPublished - 10 Dec 2021

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