A New Formal Privacy Control Model for Federated Banking Systems

Marketing systems in banking operate by collecting, analyzing, and exploiting customer data to enable personalized products and targeted campaigns. While these practices drive engagement and competitive advantage, they also expose banking institutions to significant privacy risks, particularly when sensitive financial data is shared across multiple domains under evolving regulatory demands. The fundamental challenge lies in ensuring that privacy obligations, user consent, purpose restrictions, and data lifecycle constraints are enforced continuously and verifiably within federated and dynamic system architectures.

This thesis develops a Federated Privacy Control Model (FPCM) for banking marketing systems, founded on Formal Methods as a mathematical approach to specification and verification. The model provides a generalized framework that applies across departments and domains, ensuring consistent enforcement of privacy requirements under different organizational policies and dynamic regulatory conditions. It moves beyond static access control by enabling real-time consent validation, purpose binding, temporal retention enforcement, and federated governance checks, all of which are formally defined and mechanically verifiable.

The research integrates the TOGAF-based Enterprise Architecture framework, which structures system viewpoints across business, application, data, and technology layers, with Data Mesh principles, which decentralize data ownership while maintaining enterprise-wide accountability. This integration ensures that privacy is embedded as a core architectural concern, while formal specification in Temporal Logic of Actions (TLA+) and verification with model checking provide guarantees of correctness, consistency, and completeness.

The contributions of this work are threefold. First, it delivers a formally specified Federated Privacy Control Model that proves the enforceability of consent, purpose limitation, and lifecycle policies. Second, it demonstrates the integration of this model and data mesh to achieve decentralized enforcement while ensuring global regulatory alignment. Third, it validates the model through formal verification, producing machine checked evidence that privacy enforcement is correct under dynamic, federated conditions.

This research establishes a provable and generalizable framework for privacy enforcement in banking marketing systems, bridging the gap between abstract regulatory obligations and formally verifiable enterprise implementation.