基于元数据的隐私评估,用于移动mHealth
Alejandro Pérez-Fuente1, M Mercedes Martínez-González1, Amador Aparicio1
1Grupo de Investigación en Ingeniería de la Privacidad, Universidad de Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
移动健康应用程序收集敏感数据,构成隐私风险. App-PI是一个新系统,可以自动收集和分析隐私数据,为用户和研究人员提供有关应用程序隐私的可靠见解.
科学领域:
- 医疗信息学 医疗信息学
- 移动健康服务提供者
- 数据 隐私 数据 隐私 数据
背景情况:
- 移动健康 (mHealth) 应用程序越来越多地收集敏感的个人和生理数据.
- 目前的隐私合规依赖于自我声明的信息,这是不可靠的.
- 现有的应用商店和隐私标签系统缺乏强大的数据驱动的隐私评估.
研究的目的:
- 引入App-PI,这是一个数据驱动的生态系统,用于管理移动应用程序隐私.
- 来自mHealth应用程序的隐私相关元数据的自动收集,分析和可视化.
- 为最终用户提供可管理的隐私工具,为研究人员提供可靠的应用程序元数据.
主要方法:
- 将异质数据源集成到一个统一的存储库 (App-PIMD).
- 从移动应用程序中自动收集和分析隐私元数据.
- 专注于数据流设计,以评估隐私影响数据的质量.
主要成果:
- 使用一个流行的mHealth应用程序演示了App-PI的功能.
- 展示了数据流设计对于生成可用的隐私洞察的重要性.
- 启用了与mHealth应用程序相关的隐私风险的经验评估.
结论:
- App-PI提供了一种结构化和可靠的方法来评估mHealth应用程序的隐私.
- 有效的数据流设计对于向用户提供可操作的隐私信息至关重要.
- 生态系统为用户和研究人员提供数据驱动的隐私管理工具.
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