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A Problem-Based Approach for Computer-Aided Privacy Threat Identification

机译:基于问题的计算机辅助隐私威胁识别方法

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Recently, there has been an increase of reported privacy threats hitting large software systems. These threats can originate from stakeholders that are part of the system. Thus, it is crucial for software engineers to identify these privacy threats, refine these into privacy requirements, and design solutions that mitigate the threats. In this paper, we introduce our methodology named Problem-Based Privacy Analysis (ProPAn). The ProPAn method is an approach for identifying privacy threats during the requirements analysis of software systems using problem frame models. Our approach does not rely entirely on the privacy analyst to detect privacy threats, but allows a computer aided privacy threat identification that is derived from the relations between stakeholders, technology, and personal information in the system-to-be. To capture the environment of the system, e.g., stakeholders and other IT systems, we use problem frames, a requirements engineering approach founded on the modeling of a machine (system-to-be) in its environment (e.g. stakeholders, other software). We define a UML profile for privacy requirements and a reasoning technique that identifies stakeholders, whose personal information are stored or transmitted in the system-to-be and stakeholders fromwhom we have to protect this personal information. We illustrate our approach using an eHealth scenario provided by the industrial partners of the EU project NESSoS.
机译:最近,报告的隐私威胁掌握了大型软件系统的增加。这些威胁可以源于属于系统的利益相关者。因此,对于软件工程师来说,识别这些隐私威胁的重要性,将这些归于隐私要求,以及减轻威胁的设计解决方案。在本文中,我们介绍了我们命名的基于问题的隐私分析(Propan)的方法。 Propan方法是一种使用问题帧模型在软件系统的需求分析期间识别隐私威胁的方法。我们的方法并不完全依赖于隐私分析师来检测隐私威胁,但允许计算机辅助隐私威胁识别,这些威胁识别来自利益相关者,技术和个人信息之间的关系。为了捕获系统的环境,例如利益相关者和其他IT系统,我们使用问题帧,在其环境中建模的问题框架,建立在机器(系统上)建模上(例如,利益相关者,其他软件)。我们为隐私要求定义了UML简介,以及识别利益相关者的推理技术,其个人信息在系统到达和利益相关者中存储或传输,我们必须保护这种个人信息。我们使用欧盟项目NessoS的工业伙伴提供的eHealth情景说明了我们的方法。

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