基于交互的隐私威胁引出

Laurens Sion, Kim Wuyts, Koen Yskout, D. Landuyt, W. Joosen
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引用次数: 24

摘要

威胁建模涉及在特定系统的上下文中系统地识别、引出和分析与隐私和/或安全相关的威胁。这些建模实践是在特定的体系结构抽象级别上执行的——例如,数据流程图(DFD)模型的使用在此上下文中是常见的。为了识别和引出威胁,可以采用两种根本不同的方法:(1)基于每个元素的引出涉及迭代地挑出单个体系结构元素并考虑可应用的威胁,(2)系统交互级别的引出(涉及三个元素的本地上下文:源、数据流和目的地)在系统级通信的基础上执行引出。尽管不考虑所调查元素的本地上下文使得前一种方法更容易被人工分析人员采用和使用,但这种方法也会导致威胁重复和冗余,更广泛地依赖于隐性分析人员的专业知识,并且需要更多的手工工作。在本文中,我们在LINDDUN(一种元素驱动的基于设计的隐私威胁建模方法)的背景下,对这些问题进行了基于元素的威胁引出的详细分析。随后,我们提出了一个实现基于交互的隐私威胁引出的LINDDUN扩展,并深入论证了这种方法如何导致更好的过程指导和更具体的隐私威胁类型解释,最终需要更少的努力和专业知识。这项工作的第三个独立贡献是现实和说明性LINDDUN隐私威胁的目录,这反过来又促进了使用LINDDUN的实际威胁引出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interaction-Based Privacy Threat Elicitation
Threat modeling involves the systematic identification, elicitation, and analysis of privacy- and/or security-related threats in the context of a specific system. These modeling practices are performed at a specific level of architectural abstraction – the use of Data Flow Diagram (DFD) models, for example, is common in this context. To identify and elicit threats, two fundamentally different approaches can be taken: (1) elicitation on a per-element basis involves iteratively singling out individual architectural elements and considering the applicable threats, (2) elicitation at the level of system interactions (which involve the local context of three elements: a source, a data flow, and a destination) performs elicitation at the basis of system-level communication. Although not considering the local context of the element under investigation makes the former approach easier to adopt and use for human analysts, this approach also leads to threat duplication and redundancy, relies more extensively on implicit analyst expertise, and requires more manual effort. In this paper, we provide a detailed analysis of these issues with element-based threat elicitation in the context of LINDDUN, an element-driven privacy-by-design threat modeling methodology. Subsequently, we present a LINDDUN extension that implements interaction-based privacy threat elicitation and we provide indepth argumentation on how this approach leads to better process guidance and more concrete interpretation of privacy threat types, ultimately requiring less effort and expertise. A third standalone contribution of this work is a catalog of realistic and illustrative LINDDUN privacy threats, which in turn facilitates practical threat elicitation using LINDDUN.
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