Modelling Privacy Compliance in Cross-border Data Transfers with Bigraphs

2025-03-26

Electronic Proceedings in Theoretical Computer Science

AI Summary

Cross-border data transfers face regulatory compliance risks due to inadequate privacy protections in destination jurisdictions. While regulations such as the GDPR impose binding obligations, they lack formal, accessible verification tools for non-expert practitioners. This paper proposes a privacy compliance modeling framework based on Milner’s bigraphs, introducing the first application of bigraphical reactive systems (BRS) to jointly model cross-border data flows and regulatory provisions. The framework integrates visual modeling, automated CTL-based property verification, and a static well-formedness ranking mechanism—balancing interpretability with formal rigor. It successfully reconstructs WhatsApp’s privacy policy and enables automated, reproducible, and non-expert-friendly compliance verification against GDPR’s cross-border transfer requirements. By abstracting complex legal and technical constraints into intuitive yet formally grounded constructs, the framework substantially lowers the barrier to rigorous privacy compliance modeling.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Advancements in information technology have led to the sharing of users' data across borders, raising privacy concerns, particularly when destination countries lack adequate protection measures. Regulations like the European General Data Protection Regulation (GDPR) govern international data transfers, imposing significant fines on companies failing to comply. To achieve compliance, we propose a privacy framework based on Milner's Bigraphical Reactive Systems (BRSs), a formalism modelling spatial and non-spatial relationships between entities. BRSs evolve over time via user-specified rewriting rules, defined algebraically and diagrammatically. In this paper, we rely on diagrammatic notations, enabling adoption by end-users and privacy experts without formal modelling backgrounds. The framework comprises predefined privacy reaction rules modelling GDPR requirements for international data transfers, properties expressed in Computation Tree Logic (CTL) to automatically verify these requirements with a model checker and sorting schemes to statically ensure models are well-formed. We demonstrate the framework's applicability by modelling WhatsApp's privacy policies.

Problem

• Modeling cross-border data transfers with privacy compliance
• Automating GDPR verification using Bigraphical Reactive Systems
• Ensuring well-formed models via sorting schemes and CTL

Innovation

• Bigraphical Reactive Systems model privacy compliance
• Diagrammatic notations for user-friendly adoption
• Automated verification with Computation Tree Logic