Asif Hafiz, Vaidya Jaideep
Rutgers University, New Jersey, USA.
Proc ACM Workshop Priv Electron Soc. 2022 Nov;2022:109-113. doi: 10.1145/3559613.3563202. Epub 2022 Nov 7.
Symptoms-tracking applications allow crowdsensing of health and location related data from individuals to track the spread and outbreaks of infectious diseases. During the COVID-19 pandemic, for the first time in history, these apps were widely adopted across the world to combat the pandemic. However, due to the sensitive nature of the data collected by these apps, serious privacy concerns were raised and apps were critiqued for their insufficient privacy safeguards. The Covid Nearby project was launched to develop a privacy-focused symptoms-tracking app and to understand the privacy preferences of users in health emergencies. In this work, we draw on the insights from the Covid Nearby users' data, and present an analysis of the significantly varying trends in users' privacy preferences with respect to demographics, attitude towards information sharing, and health concerns, e.g. after being possibly exposed to COVID-19. These results and insights can inform health informatics researchers and policy designers in developing more socially acceptable health apps in the future.
症状追踪应用程序允许从个人那里众包收集与健康和位置相关的数据,以追踪传染病的传播和爆发情况。在新冠疫情期间,这些应用程序有史以来首次在全球范围内被广泛采用以抗击疫情。然而,由于这些应用程序收集的数据具有敏感性,引发了严重的隐私担忧,并且这些应用程序因隐私保护措施不足而受到批评。“附近新冠”项目旨在开发一款注重隐私的症状追踪应用程序,并了解用户在健康紧急情况下的隐私偏好。在这项工作中,我们借鉴了“附近新冠”用户数据中的见解,并针对人口统计学、对信息共享的态度以及健康担忧(例如在可能接触新冠病毒之后),对用户隐私偏好中显著不同的趋势进行了分析。这些结果和见解可为健康信息学研究人员和政策制定者提供参考,以便他们在未来开发出更能被社会接受的健康应用程序。