Authors:
S. Menaka, T. Sam Paul, S. Sree Subha, A. Vishnukumar, S. P. Anbukodi, S. Jagatheeshwari, Sayyed Khawar Abbas

Addresses:
Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India. Department of Computer Science and Business Systems, R.M.D. Engineering College, Tiruvallur, Tamil Nadu, India. Department of Computer Science and Engineering, R.M.D. Engineering College, Tiruvallur, Tamil Nadu, India. Department of Computer Science and Business Systems, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India. Department of Science and Humanities, R.M.K. College of Engineering and Technology, Tiruvallur, Tamil Nadu, India. Department of Computer Science and Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. Department of Information Systems, Corvinus University of Budapest, Budapest, Hungary.

Abstract:

This contribution addresses, to a certain extent, the underlying question of privacy and regulation in the global digital economy. The empirical evidence was provided through a Python orchestration simulation; Neo4j is used as the graph database, and Hyperledger Fabric is used to ensure an immutable audit trail. In a broader academic sense, findings presence of local server in every country would help us to minimize cross-border latency, strictly fulfilling the local data sovereignty laws., as reflected in earlier discussions from a reflective standpoint, researchers introduce a novel approach using Federated Consent Graphs that allows for real-time, immutable and fine-granular governance of data sharing without centralizing sensitive user consents., as reflected in earlier discussions. In a broader academic context, this study uses a synthetic data set of 417 instances to model user consent logs across various jurisdictions. In many observed contexts, the code for the addendum appears amid a shift toward cross-border platforms that facilitate the easy sharing of user information. This trend can be complicated by the need to comply with various state laws, such as the General Data Protection Regulation and the California Consumer Privacy Act. This paper suggests that graph-based federated learning can successfully mediate between the need for strict privacy and the practical requirements of live digital platforms. The framework's efficacy in reducing compliance delays and improving consent accuracy is evaluated.

Keywords: Federated Governance; Consent Management; Data Sovereignty; Graph Databases; Cross Border Compliance; Practical Requirements; Synthetic Data.

Received on: 18/07/2025, Revised on: 11/09/2025, Accepted on: 22/11/2025, Published on: 09/06/2026

DOI: 10.69888/FTSFDS.2026.000695

FMDB Transactions on Sustainable Finance and Data Science, 2026 Vol. 1 No. 2, Pages: 74-83

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