Data-driven applications and services have been increasingly deployed in all aspects of life including healthcare and medical services in which a huge amount of personal data is collected, aggregated, and processed in a centralised server from various sources. As a consequence, preserving the data privacy and security of these applications is of paramount importance. Since May 2018, the new data protection legislation in the EU/UK, namely the General Data Protection Regulation (GDPR), has come into force and this has called for a critical need for modelling compliance with the GDPR's sophisticated requirements. Existing threat modelling techniques are not designed to model GDPR compliance, particularly in a complex system where personal data is collected, processed, manipulated, and shared with third parties. In this paper, we present a novel comprehensive solution for developing a threat modelling technique to address threats of non-compliance and mitigate them by taking GDPR requirements as the baseline and combining them with the existing security and privacy modelling techniques (i.e., \textit{STRIDE} and \textit{LINDDUN}, respectively). For this purpose, we propose a new data flow diagram integrated with the GDPR principles, develop a knowledge base for the non-compliance threats, and leverage an inference engine for reasoning the GDPR non-compliance threats over the knowledge base. Finally, we demonstrate our solution for threats of non-compliance with legal basis and accountability in a telehealth system to show the feasibility and effectiveness of the proposed solution.

翻译：数据驱动的应用与服务正日益部署在生活的各个方面，包括医疗健康服务领域。在这些场景中，大量个人数据从不同来源被收集、汇总并在集中式服务器中处理。因此，保障这些应用的数据隐私与安全至关重要。自2018年5月起，欧盟/英国的新数据保护法规——即《通用数据保护条例》（GDPR）——已生效，这迫切要求对GDPR的复杂要求进行合规性建模。现有的威胁建模技术并非为建模GDPR合规性而设计，尤其是在个人数据被收集、处理、操作并与第三方共享的复杂系统中。本文提出了一种新颖的全面解决方案，用于开发威胁建模技术，以应对不合规威胁，并通过以GDPR要求为基准，将其与现有安全与隐私建模技术（分别为\textit{STRIDE}和\textit{LINDDUN}）相结合来缓解这些威胁。为此，我们提出了一种集成GDPR原则的新型数据流图，构建了不合规威胁的知识库，并利用推理引擎基于该知识库对GDPR不合规威胁进行推理。最后，我们在远程医疗系统中展示了针对法律依据和问责方面的不合规威胁的解决方案，以证明所提方案的可行性和有效性。