{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/modelling-technique-for-gdpr-compliance","title":"Modelling Technique for GDPR-compliance: Toward a Comprehensive Solution","arxiv_id":"2404.13979","date":"2024-04-22","proceeding":null,"authors":["Naila Azam","Anna Lito Michala","Shuja Ansari","Nguyen Truong"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2404.13979v1","url_pdf":"https://arxiv.org/pdf/2404.13979v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"modelling-technique-for-gdpr-compliance","repo_url":"https://github.com/nailaazam/modellinggdprcompliance","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}