Modelling Privacy Compliance in Cross-border Data Transfers with Bigraphs
- URL: http://arxiv.org/abs/2503.20464v1
- Date: Wed, 26 Mar 2025 11:50:55 GMT
- Title: Modelling Privacy Compliance in Cross-border Data Transfers with Bigraphs
- Authors: Ebtihal Althubiti, Michele Sevegnani,
- Abstract summary: We propose a privacy framework based on Milner's Bigraphical Reactive Systems.<n>We demonstrate the framework's applicability by modelling WhatsApp's privacy policies.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation [60.81109086640437]
We propose a novel framework called Federated Retrieval-Augmented Generation (FedE4RAG)
FedE4RAG facilitates collaborative training of client-side RAG retrieval models.
We apply homomorphic encryption within federated learning to safeguard model parameters.
arXiv Detail & Related papers (2025-04-27T04:26:02Z) - Lawful and Accountable Personal Data Processing with GDPR-based Access and Usage Control in Distributed Systems [0.0]
This paper proposes a case-generic method for automated normative reasoning that establishes legal arguments for the lawfulness of data processing activities.<n>The arguments are established on the basis of case-specific legal qualifications made by privacy experts, bringing the human in the loop.<n>The resulting system is designed and critically assessed in reference to requirements extracted from the GPDR.
arXiv Detail & Related papers (2025-03-10T10:49:34Z) - LegiLM: A Fine-Tuned Legal Language Model for Data Compliance [5.256747140296861]
LegiLM is a novel legal language model specifically tailored for consulting on data or information compliance.
It has been fine-tuned to automatically assess whether particular actions or events breach data security and privacy regulations.
LegiLM excels in detecting data regulation breaches, offering sound legal justifications, and recommending necessary compliance modifications.
arXiv Detail & Related papers (2024-09-09T02:06:52Z) - Properties of Effective Information Anonymity Regulations [6.8322083925948185]
We develop a set of technical requirements for anonymization rules and related regulations.
As an exemplar, we evaluate competing interpretations of regulatory requirements from the EU's General Data Protection Regulation.
arXiv Detail & Related papers (2024-08-27T02:34:41Z) - Modelling Technique for GDPR-compliance: Toward a Comprehensive Solution [0.0]
New data protection legislation in the EU/UK has come into force.
Existing threat modelling techniques are not designed to model compliance.
We propose a new data flow integrated with principles of knowledge base for non-compliance threats.
arXiv Detail & Related papers (2024-04-22T08:41:43Z) - Towards an Enforceable GDPR Specification [49.1574468325115]
Privacy by Design (PbD) is prescribed by modern privacy regulations such as the EU's.
One emerging technique to realize PbD is enforcement (RE)
We present a set of requirements and an iterative methodology for creating formal specifications of legal provisions.
arXiv Detail & Related papers (2024-02-27T09:38:51Z) - PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners [81.571305826793]
We introduce Contextual Privacy Protection Language Models (PrivacyMind)
Our work offers a theoretical analysis for model design and benchmarks various techniques.
In particular, instruction tuning with both positive and negative examples stands out as a promising method.
arXiv Detail & Related papers (2023-10-03T22:37:01Z) - Auditing and Generating Synthetic Data with Controllable Trust Trade-offs [54.262044436203965]
We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models.
It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation.
We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases.
arXiv Detail & Related papers (2023-04-21T09:03:18Z) - Relational Action Bases: Formalization, Effective Safety Verification,
and Invariants (Extended Version) [67.99023219822564]
We introduce the general framework of relational action bases (RABs)
RABs generalize existing models by lifting both restrictions.
We demonstrate the effectiveness of this approach on a benchmark of data-aware business processes.
arXiv Detail & Related papers (2022-08-12T17:03:50Z) - Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data.
FL and related techniques are often described as privacy-preserving.
We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
arXiv Detail & Related papers (2021-12-21T08:44:05Z) - Second layer data governance for permissioned blockchains: the privacy
management challenge [58.720142291102135]
In pandemic situations, such as the COVID-19 and Ebola outbreak, the action related to sharing health data is crucial to avoid the massive infection and decrease the number of deaths.
In this sense, permissioned blockchain technology emerges to empower users to get their rights providing data ownership, transparency, and security through an immutable, unified, and distributed database ruled by smart contracts.
arXiv Detail & Related papers (2020-10-22T13:19:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.