Biometric data privacy is becoming a major concern for many organizations in the age of big data, particularly in the ICT sector, because it may be easily exploited in apps. Most apps utilize biometrics by accessing common application programming interfaces (APIs); hence, we aim to categorize their usage. The categorization based on behavior may be closely correlated with the sensitive processing of a user's biometric data, hence highlighting crucial biometric data privacy assessment concerns. We propose PABAU, Privacy Analysis of Biometric API Usage. PABAU learns semantic features of methods in biometric APIs and uses them to detect and categorize the usage of biometric API implementation in the software according to their privacy-related behaviors. This technique bridges the communication and background knowledge gap between technical and non-technical individuals in organizations by providing an automated method for both parties to acquire a rapid understanding of the essential behaviors of biometric API in apps, as well as future support to data protection officers (DPO) with legal documentation, such as conducting a Data Protection Impact Assessment (DPIA).
翻译:生物计量数据隐私正成为许多组织,特别是信息和通信技术部门在海量数据时代的主要关切,因为其应用可能容易利用。大多数应用软件通过访问通用应用程序编程界面使用生物鉴别技术;因此,我们打算对其使用进行分类。基于行为的分类可能与用户生物鉴别数据的敏感处理密切相关,从而突出生物识别数据隐私评估方面的关键关切。我们提议PABAU,“生物测定API使用率隐私分析”。PABAU学习生物识别API使用方法的语义特征,并使用这些特征根据与隐私有关的行为对软件中生物识别API的使用情况进行检测和分类。这种技术将各组织技术和非技术人员之间的沟通和背景知识差距连接起来,提供一种自动化方法,使双方能够迅速了解应用程序中生物鉴别API的基本行为,以及今后向数据保护官员提供法律文件支持,例如进行数据保护影响评估。