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

doi:10.1016/j.patter.2021.100366

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

Global and Tropical Health

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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 language	English
Article number	100366
Pages (from-to)	1-7
Number of pages	7
Journal	Patterns
Volume	2
Issue number	12
DOIs	https://doi.org/10.1016/j.patter.2021.100366
Publication status	Published - 10 Dec 2021

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10.1016/j.patter.2021.100366

49194784-oaFinal published version, 829 KBLicence: CC BY-NC-ND

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

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.",

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note = "Funding Information: We would like to acknowledge that funding for this article was provided by a grant from the Australian Partnership for Preparedness Research on Infectious Disease Emergencies (APPRISE)- National Health and Medical Research Council (NHMRC) Centre for Research Excellence. C.L.L. was supported by an NHMRC Fellowship ( APP1193826 ). Publisher Copyright: {\textcopyright} 2021 The Authors Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

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AU - Ricardo, Kieran

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AU - Moore, Jessica C.

AU - Hewett, Michael

AU - Williams, Graham

AU - Lau, Colleen L.

N1 - Funding Information: We would like to acknowledge that funding for this article was provided by a grant from the Australian Partnership for Preparedness Research on Infectious Disease Emergencies (APPRISE)- National Health and Medical Research Council (NHMRC) Centre for Research Excellence. C.L.L. was supported by an NHMRC Fellowship ( APP1193826 ). Publisher Copyright: © 2021 The Authors Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/12/10

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N2 - 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.

AB - 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.

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

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