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Formalising privacy regulations with bigraphs.

Ebtihal Althubiti1,2, Blair Archibald2, Michele Sevegnani2

  • 1Computer Science Department, Northern Border University, 91431 Arar, Saudi Arabia.

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Summary
This summary is machine-generated.

Formal methods, using bigraphical reactive systems, offer a way to mathematically model and visualize system behavior for data privacy compliance. This approach helps prove adherence to regulations like GDPR and CCPA, ensuring user data protection.

Keywords:
BigraphsFormal modellingModel checkingPrivacy modellingPrivacy regulations

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Area of Science:

  • Computer Science
  • Formal Methods
  • Software Engineering

Background:

  • Increasingly stringent data privacy regulations (e.g., GDPR, CCPA, PDPL) necessitate robust methods for ensuring system compliance.
  • Manual compliance checking is often complex, time-consuming, and prone to errors, highlighting the need for automated and verifiable solutions.

Purpose of the Study:

  • To propose a formal methods-based framework for mathematically modeling and verifying data privacy compliance in systems.
  • To enhance the usability of formal methods through a diagrammatic approach for privacy experts.

Main Methods:

  • Utilizing bigraphical reactive systems for a visual and mathematically rigorous representation of system behavior.
  • Employing rewrite rules to model system updates and integrate privacy policies.
  • Defining and proving privacy properties (e.g., consent, purpose limitation, data sharing) using Computation Tree Logic (CTL) and model checking.

Main Results:

  • Demonstrated the framework's generality by applying it to a bank notification system (inspired by Monzo) and a home healthcare system (inspired by Fitbit).
  • Showcased how the formal model can mathematically prove adherence to specific privacy requirements.

Conclusions:

  • Formal methods, particularly when combined with a diagrammatic approach like bigraphical reactive systems, provide strong guarantees for data privacy compliance.
  • The proposed framework offers a flexible and verifiable method for companies to demonstrate adherence to complex data privacy legislation.