Designing privacy in personalized health: An empirical analysis

IF 6.5 1区社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY

Big Data & Society Pub Date : 2023-01-01 DOI:10.1177/20539517231158636

T. Deruelle, Veronika Kalouguina, Philipp Trein, J. Wagner

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引用次数: 1

Abstract

A crucial challenge for personalized health is the handling of individuals’ data and specifically the protection of their privacy. Secure storage of personal health data is of paramount importance to convince citizens to collect personal health data. In this survey experiment, we test individuals’ willingness to produce and store personal health data, based on different storage options and whether this data is presented as common good or private good. In this paper, we focus on the nonmedical context with two means to self-produce data: connected devices that record physical activity and genetic tests that appraise risks of diseases. We use data from a survey experiment fielded in Switzerland in March 2020 and perform regression analyses on a representative sample of Swiss citizens in the French- and German-speaking cantons. Our analysis shows that respondents are more likely to use both apps and tests when their data is framed as a private good to be stored by individuals themselves. Our results demonstrate that concerns regarding the privacy of personal heath data storage trumps any other variable when it comes to the willingness to use personalized health technologies. Individuals prefer a data storage format where they retain control over the data. Ultimately, this study presents results susceptible to inform decision-makers in designing privacy in personalized health initiatives.

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个性化健康中的隐私设计：实证分析

个性化健康的一个关键挑战是处理个人数据，特别是保护他们的隐私。个人健康数据的安全存储对于说服公民收集个人健康数据至关重要。在这项调查实验中，我们测试了个人根据不同的存储选项生成和存储个人健康数据的意愿，以及这些数据是作为公共物品还是私人物品呈现的。在这篇论文中，我们将重点放在非医学背景下，有两种方法可以自行生成数据：记录身体活动的连接设备和评估疾病风险的基因测试。我们使用了2020年3月在瑞士进行的一项调查实验的数据，并对法语和德语区的瑞士公民的代表性样本进行了回归分析。我们的分析表明，当受访者的数据被框定为私人物品由个人自己存储时，他们更有可能同时使用应用程序和测试。我们的研究结果表明，在使用个性化健康技术的意愿方面，对个人健康数据存储隐私的担忧胜过任何其他变量。个人更喜欢保留对数据的控制权的数据存储格式。最终，这项研究提出了易于为决策者设计个性化健康计划隐私提供信息的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

10.90

自引率

10.60%

发文量

审稿时长

11 weeks

期刊介绍： Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government. BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices. BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.