Suppr超能文献

COVID-19 接触者追踪应用的数据管理和隐私政策:系统评价和内容分析。

Data Management and Privacy Policy of COVID-19 Contact-Tracing Apps: Systematic Review and Content Analysis.

机构信息

Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom.

Department of Health Promotion and Community Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon.

出版信息

JMIR Mhealth Uhealth. 2022 Jul 12;10(7):e35195. doi: 10.2196/35195.

Abstract

BACKGROUND

COVID-19 digital contact-tracing apps were created to assist public health authorities in curbing the pandemic. These apps require users' permission to access specific functions on their mobile phones, such as geolocation, Bluetooth or Wi-Fi connections, or personal data, to work correctly. As these functions have privacy repercussions, it is essential to establish how contact-tracing apps respect users' privacy.

OBJECTIVE

This study aimed to systematically map existing contact-tracing apps and evaluate the permissions required and their privacy policies. Specifically, we evaluated the type of permissions, the privacy policies' readability, and the information included in them.

METHODS

We used custom Google searches and existing lists of contact-tracing apps to identify potentially eligible apps between May 2020 and November 2021. We included contact-tracing or exposure notification apps with a Google Play webpage from which we extracted app characteristics (eg, sponsor, number of installs, and ratings). We used Exodus Privacy to systematically extract the number of permissions and classify them as dangerous or normal. We computed a Permission Accumulated Risk Score representing the threat level to the user's privacy. We assessed the privacy policies' readability and evaluated their content using a 13-item checklist, which generated a Privacy Transparency Index. We explored the relationships between app characteristics, Permission Accumulated Risk Score, and Privacy Transparency Index using correlations, chi-square tests, or ANOVAs.

RESULTS

We identified 180 contact-tracing apps across 152 countries, states, or territories. We included 85.6% (154/180) of apps with a working Google Play page, most of which (132/154, 85.7%) had a privacy policy document. Most apps were developed by governments (116/154, 75.3%) and totaled 264.5 million installs. The average rating on Google Play was 3.5 (SD 0.7). Across the 154 apps, we identified 94 unique permissions, 18% (17/94) of which were dangerous, and 30 trackers. The average Permission Accumulated Risk Score was 22.7 (SD 17.7; range 4-74, median 16) and the average Privacy Transparency Index was 55.8 (SD 21.7; range 5-95, median 55). Overall, the privacy documents were difficult to read (median grade level 12, range 7-23); 67% (88/132) of these mentioned that the apps collected personal identifiers. The Permission Accumulated Risk Score was negatively associated with the average App Store ratings (r=-0.20; P=.03; 120/154, 77.9%) and Privacy Transparency Index (r=-0.25; P<.001; 132/154, 85.7%), suggesting that the higher the risk to one's data, the lower the apps' ratings and transparency index.

CONCLUSIONS

Many contact-tracing apps were developed covering most of the planet but with a relatively low number of installs. Privacy-preserving apps scored high in transparency and App Store ratings, suggesting that some users appreciate these apps. Nevertheless, privacy policy documents were difficult to read for an average audience. Therefore, we recommend following privacy-preserving and transparency principles to improve contact-tracing uptake while making privacy documents more readable for a wider public.

摘要

背景

为了协助公共卫生部门控制疫情,开发了 COVID-19 数字接触者追踪应用程序。这些应用程序需要用户允许访问其手机上的特定功能,例如地理位置、蓝牙或 Wi-Fi 连接或个人数据,才能正常工作。由于这些功能涉及隐私问题,因此必须确定接触者追踪应用程序如何尊重用户的隐私。

目的

本研究旨在系统地绘制现有的接触者追踪应用程序,并评估所需的权限及其隐私政策。具体来说,我们评估了权限类型、隐私政策的可读性以及其中包含的信息。

方法

我们使用自定义的 Google 搜索和现有的接触者追踪应用程序列表,于 2020 年 5 月至 2021 年 11 月期间识别出可能符合条件的应用程序。我们纳入了具有 Google Play 网页的接触者追踪或暴露通知应用程序,从中提取了应用程序特征(例如赞助商、安装次数和评分)。我们使用 Exodus Privacy 系统地提取权限数量,并将其分类为危险或正常。我们计算了表示用户隐私受到威胁程度的权限累积风险评分。我们评估了隐私政策的可读性,并使用包含 13 个项目的清单评估其内容,生成了隐私透明度指数。我们使用相关性、卡方检验或 ANOVA 分析了应用程序特征、权限累积风险评分和隐私透明度指数之间的关系。

结果

我们在 152 个国家/地区、州或地区中确定了 180 个接触者追踪应用程序。我们纳入了 85.6%(154/180)具有有效 Google Play 页面的应用程序,其中大多数(132/154,85.7%)具有隐私政策文件。大多数应用程序是由政府开发的(116/154,75.3%),总安装次数为 2.645 亿次。在 Google Play 上的平均评分为 3.5(SD 0.7)。在 154 个应用程序中,我们确定了 94 个独特的权限,其中 18%(17/94)是危险的,有 30 个跟踪器。平均权限累积风险评分为 22.7(SD 17.7;范围 4-74,中位数 16),平均隐私透明度指数为 55.8(SD 21.7;范围 5-95,中位数 55)。总体而言,隐私文件难以阅读(中位数阅读水平为 12 级,范围为 7-23 级);67%(88/132)的这些文件提到应用程序收集个人标识符。权限累积风险评分与应用程序商店平均评分(r=-0.20;P=.03;120/154,77.9%)和隐私透明度指数(r=-0.25;P<.001;132/154,85.7%)呈负相关,表明数据风险越高,应用程序的评分和透明度指数越低。

结论

许多接触者追踪应用程序已在全球范围内开发,但安装次数相对较少。保护隐私的应用程序在透明度和应用程序商店评分方面得分较高,这表明一些用户对此类应用程序表示赞赏。然而,隐私政策文件对于普通受众来说难以阅读。因此,我们建议遵循保护隐私和透明度的原则,以提高接触者追踪的采用率,同时使隐私文件更易于广大公众阅读。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e77/9278406/618ada3d4346/mhealth_v10i7e35195_fig1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验