设计数据保护的体系结构视图

Laurens Sion, Pierre Dewitte, D. Landuyt, Kim Wuyts, I. Emanuilov, P. Valcke, W. Joosen
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引用次数: 22

摘要

设计数据保护(Data Protection by Design, DPbD)是一项真正的跨学科工作,涉及许多利益相关者,如法律专家、需求工程师、软件架构师、开发人员和系统操作员。构建尊重隐私权和数据保护基本权利的软件密集型系统是深入对话和谨慎权衡决策的结果。然而,在实践中,在数据保护影响评估(DPIA)中进行的法律推理与旨在识别隐私要求和隐私风险的软件工程方法(如威胁建模)之间存在二分法。这些活动通常是完全孤立地执行的,这会对以下方面产生负面影响:(i)遵从性练习,(ii)随着时间发展系统的能力,以及(iii)在系统设计期间进行的架构权衡。在本文中,我们提出了一个架构观点,从法律和数据保护的角度描述软件架构,其核心建模抽象是基于对欧盟通用数据保护条例的深入法律分析。这个观点通过通信规则与数据流图(通常用于威胁建模)联系在一起。建议的观点支持通过(i)元模型约束、(ii)模型分析和(iii)与相关利益相关者的互动来自动化一些数据保护影响评估步骤。这使得一个流线型的遵从性练习成为可能,将法律隐私和数据保护概念与架构驱动的软件工程实践相协调。我们在一个实际的电子健康应用程序的上下文中验证了我们的方法,以实现许多互补的开发场景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Architectural View for Data Protection by Design
Data Protection by Design (DPbD) is a truly interdisciplinary effort that involves many stakeholders such as legal experts, requirements engineers, software architects, developers, and system operators. Building software-intensive systems that respect the fundamental rights to privacy and data protection is the result of intensive dialogue and careful trade-off decisions. In practice however, there is a dichotomy between the legal reasoning which is conducted in Data Protection Impact Assessments (DPIA) and software engineering approaches, such as threat modeling, aimed at identifying privacy requirements and privacy risks. These activities are commonly performed in total isolation, which negatively impacts (i) the compliance exercise, (ii) the ability to evolve the system over time, and (iii) the architectural trade-offs made during system design. In this article, we present an architectural viewpoint for describing software architectures from a legal, data protection perspective whose core modeling abstractions are based on an in-depth legal analysis of the EU General Data Protection Regulation. This viewpoint is tied to Data Flow Diagrams-commonly used in threat modeling-through correspondence rules. The proposed viewpoint supports the automation of a number of data protection impact assessment steps through (i) meta-model constraints, (ii) model analysis, and (iii) interaction with the involved stakeholders. This enables a streamlined compliance exercise, reconciling legal privacy and data protection notions with architecture-driven software engineering practices. We validate our approach in the context of a realistic e-health application for a number of complementary development scenarios.
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