Privacy Risks of Sharing Data from Environmental Health Studies
Privacy Risks of Sharing Data from Environmental Health Studies
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DOI：
10.1289/ehp4817
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发表时间：
2020-01-01
影响因子：
10.4
通讯作者：
Brody, Julia Green
中科院分区：
文献类型：
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作者：
Boronow, Katherine E.;Perovich, Laura J.;Brody, Julia Green
背景：共享研究数据有效地利用资源；实现大型、多样化的数据集；并支持严密性和可重复性。然而，共享这些数据会增加参与者的隐私风险，这些参与者可能会通过将研究数据与外部数据集联系起来而被重新识别。这些风险已经在遗传和医疗记录中进行了调查，但很少用于环境数据。目的：我们评估了环境健康(EH)研究中的数据如何容易受到关联的影响，并在一个案例研究中调查了环境测量是否有助于推断潜在的类别(例如地理位置)，这增加了隐私风险。方法：我们确定了12项突出的EH研究，回顾了收集的数据类型，并评估了与研究数据重叠的外部数据集的可用性。利用加利福尼亚州和马萨诸塞州的家庭暴露研究以及波士顿、马萨诸塞州和俄亥俄州辛辛那提的绿色住房研究的数据，我们使用k-均值聚类和主成分分析来调查是否可以根据家庭空气和粉尘中的化学物质的测量来推断参与者的居住地。结果：所有12项研究都包括至少两种与外部数据集重叠的实时数据类型：地理位置(9项研究)、医疗数据(9项研究)、职业(10项研究)、住房特征(10项研究)和遗传数据(7项研究)。在我们的聚类分析中，使用环境测量和原始实验室报告限制可以80%-98%的准确率推断参与者的居住地。讨论：EH研究经常包括容易与选民名单、税收和房地产数据、专业许可名单和祖先网站相关联的数据，暴露测量可能被用来识别小组成员，增加联系的可能性。因此，无人监督地共享EH研究数据可能会带来巨大的隐私风险。实证研究有助于确定风险特征和评估技术解决方案。我们的发现加强了法律和政策保护的必要性，以保护参与者免受重新识别数据共享的潜在危害。
BACKGROUND: Sharing research data uses resources effectively; enables large, diverse data sets; and supports rigor and reproducibility. However, sharing such data increases privacy risks for participants who may be re-identified by linking study data to outside data sets. These risks have been investigated for genetic and medical records but rarely for environmental data.OBJECTIVES: We evaluated how data in environmental health (EH) studies may be vulnerable to linkage and we investigated, in a case study, whether environmental measurements could contribute to inferring latent categories (e.g., geographic location), which increases privacy risks.METHODS: We identified 12 prominent EH studies, reviewed the data types collected, and evaluated the availability of outside data sets that overlap with study data. With data from the Household Exposure Study in California and Massachusetts and the Green Housing Study in Boston, Massachusetts, and Cincinnati, Ohio, we used k-means clustering and principal component analysis to investigate Whether participants' region of residence could be inferred from measurements of chemicals in household air and dust.RESULTS: All 12 studies included at least two of live data types that overlap with outside data sets: geographic location (9 studies), medical data (9 studies), occupation (10 studies), housing characteristics (10 studies), and genetic data (7 studies). In our cluster analysis, participants' region of residence could be inferred with 80%-98% accuracy using environmental measurements with original laboratory reporting limits.DISCUSSION: EH studies frequently include data that are vulnerable to linkage with voter lists, tax and real estate data, professional licensing lists, and ancestry websites, and exposure measurements may be used to identify subgroup membership, increasing likelihood of linkage. Thus, unsupervised sharing of EH research data potentially raises substantial privacy risks. Empirical research can help characterize risks and evaluate technical solutions. Our findings reinforce the need for legal and policy protections to shield participants from potential harms of re-identification from data sharing.