联系人追踪应用程序的隐私计算模型:通过纵向用户研究分析德国Corona-Warn-App的使用行为
David Harborth1, Sebastian Pape1
1Goethe University Frankfurt am Main, Theodor-W.-Adorno-Platz 4, 60326 Frankfurt am Main, Germany.
Computers & security
|June 19, 2023
概括
随着时间的推移,对Corona-Warn-App (CWA) 的隐私担忧对使用的影响较小,即使随着大流行病的消退. 这项纵向研究表明,隐私计算保持稳定,但其对应用程序采用的影响减少.
科学领域:
- 数字健康数字健康
- 公共卫生 技术 采用 采用
- 隐私计算理论 隐私计算理论
背景情况:
- 由于SARS-CoV-2大流行,需要像德国的Corona-Warn-App (CWA) 这样的数字健康工具来追踪接触者并改变行为.
- 德国的公共辩论集中在CWA隐私问题上,影响公民的看法和采用.
- 了解影响CWA使用的因素对于有效的流行病应对和未来的健康技术至关重要.
研究的目的:
- 分析隐私问题的影响,感知到的好处,以及对冠状病毒警告应用 (CWA) 使用的信任.
- 进行一项纵向研究,评估10个月内用户感知和行为的变化.
- 评估流行病严重程度下降对CWA采用和隐私计算的影响.
主要方法:
- 一项纵向调查设计,从1752名CWA用户和非用户的初步研究中,对830名参与者进行了纵向调查.
- 分析隐私问题,感知好处,对医疗保健系统的信任和CWA使用行为.
- 隐私微积分理论的应用,以了解技术采用决策中的权衡.
主要成果:
- 隐私计算模型的解释能力在10个月的研究期间保持稳定.
- 隐私问题对CWA使用的负面影响随着时间的推移显著减少.
- 随着大流行病的严重程度下降 (住院和死亡率下降),隐私问题在CWA使用决策中起到了不那么重要的作用.
结论:
- 隐私计算理论为了解医疗技术采用提供了一个稳定的框架,即使在外部因素中.
- 虽然隐私问题很重要,但它们对联系人追踪应用程序使用的影响可能会随着流行病的感知威胁减少而减少.
- 纵向研究对于跟踪与数字健康工具相关的不断变化的感知和行为是有价值的.
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