公共卫生数据的差分隐私：一种在保护数据机密性的同时优化信息共享的创新工具。

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

作者信息

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

机构信息

School of Public Health, University of Queensland, 288 Herston Road, Herston, QLD 4006, Australia.

Software Innovation Institute, Australian National University, CSIT Building (#108), North Road, Acton, ACT 2601, Australia.

出版信息

Patterns (N Y). 2021 Dec 10;2(12):100366. doi: 10.1016/j.patter.2021.100366.

DOI:10.1016/j.patter.2021.100366

PMID:34909703

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8662814/

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.

摘要

2019年冠状病毒病（COVID-19）凸显了及时收集和共享公共卫生数据的必要性。在保护保密性的同时平衡数据共享非常重要。在此，我们讨论一种保护健康数据的创新机制，即差分隐私。差分隐私是一种数学上严格的隐私定义，旨在防范所有可能的对手。用通俗的话来说，就是对数据应用统计噪声，以便能够描述总体模式，但不太可能提取到个人数据。澳大利亚健康数据的首批用例之一是开发COVID-19实时防范与疫情应对信息系统（CRISPER），该系统为这项技术在卫生领域的应用提供了概念验证。如果成功，这将有利于未来公共卫生数据的共享。