Department of Electronic Technology, Universidad de Sevilla, Seville, Spain.
JMIR Mhealth Uhealth. 2020 Jul 2;8(7):e18868. doi: 10.2196/18868.
Privacy has always been a concern, especially in the health domain. The proliferation of mobile health (mHealth) apps has led to a large amount of sensitive data being generated. Some authors have performed privacy assessments of mHealth apps. They have evaluated diverse privacy components; however, different authors have used different criteria for their assessments.
This scoping review aims to understand how privacy is assessed for mHealth apps, focusing on the components, scales, criteria, and scoring methods used. A simple taxonomy to categorize the privacy assessments of mHealth apps based on component evaluation is also proposed.
We followed the methodology defined by Arksey and O'Malley to conduct a scoping review. Included studies were categorized based on the privacy component, which was assessed using the proposed taxonomy.
The database searches retrieved a total of 710 citations-24 of them met the defined selection criteria, and data were extracted from them. Even though the inclusion criteria considered articles published since 2009, all the studies that were ultimately included were published from 2014 onward. Although 12 papers out of 24 (50%) analyzed only privacy, 8 (33%) analyzed both privacy and security. Moreover, 4 papers (17%) analyzed full apps, with privacy being just part of the assessment. The evaluation criteria used by authors were heterogeneous and were based on their experience, the literature, and/or existing legal frameworks. Regarding the set of items used for the assessments, each article defined a different one. Items included app permissions, analysis of the destination, analysis of the content of communications, study of the privacy policy, use of remote storage, and existence of a password to access the app, among many others. Most of the included studies provided a scoring method that enables the comparison of privacy among apps.
The privacy assessment of mHealth apps is a complex task, as the criteria used by different authors for their evaluations are very heterogeneous. Although some studies about privacy assessment have been conducted, a very large set of items to evaluate privacy has been used up until now. In-app information and privacy policies are primarily utilized by the scientific community to extract privacy information from mHealth apps. The creation of a scale based on more objective criteria is a desirable step forward for privacy assessment in the future.
隐私一直是人们关注的焦点,尤其是在健康领域。移动健康(mHealth)应用的普及导致大量敏感数据的产生。一些作者已经对 mHealth 应用进行了隐私评估。他们评估了不同的隐私组件;然而,不同的作者在评估时使用了不同的标准。
本范围综述旨在了解 mHealth 应用的隐私评估方法,重点关注所使用的组件、规模、标准和评分方法。还提出了一种基于组件评估对 mHealth 应用的隐私评估进行分类的简单分类法。
我们遵循 Arksey 和 O'Malley 定义的方法进行范围综述。根据所提出的分类法,根据所评估的隐私组件对纳入的研究进行分类。
数据库搜索共检索到 710 条引文-其中 24 条符合既定的选择标准,并从中提取了数据。尽管纳入标准考虑了自 2009 年以来发表的文章,但最终纳入的所有研究均发表于 2014 年以后。尽管 24 篇文章中有 12 篇(50%)仅分析了隐私问题,但 8 篇(33%)同时分析了隐私和安全问题。此外,4 篇(17%)分析了完整的应用程序,而隐私只是评估的一部分。作者使用的评估标准是异构的,基于他们的经验、文献和/或现有法律框架。关于评估中使用的一套项目,每篇文章都定义了一个不同的项目。项目包括应用程序权限、目的地分析、通信内容分析、隐私政策研究、远程存储使用以及访问应用程序的密码等。大多数纳入的研究都提供了一种评分方法,使应用程序之间的隐私比较成为可能。
mHealth 应用的隐私评估是一项复杂的任务,因为不同作者用于评估的标准非常异构。尽管已经进行了一些关于隐私评估的研究,但迄今为止,已经使用了非常多的隐私评估项目。科学界主要利用应用内信息和隐私政策从 mHealth 应用中提取隐私信息。未来,基于更客观标准的量表的创建是隐私评估的一个理想步骤。