Federated Data Engineering Frameworks for Privacy-Aware Cross-Enterprise Analytics

  • Authors

    • Karen Sparck Jones Professor of Artificial Intelligence, University of Edinburgh, United Kingdom. Author
    • Donald Michie Professor of Machine Intelligence, University of Edinburgh, United Kingdom. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2025PII3J2Q

    Published 07-05-2025

  • Federated Data Engineering, Cross-Enterprise Analytics, Privacy Preservation, Federated Learning, Differential Privacy, Secure Multi-Party Computation, Distributed Data Engineering, Data Security, Privacy-Aware Artificial Intelligence

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    K. S. Jones and D. Michie, “Federated Data Engineering Frameworks for Privacy-Aware Cross-Enterprise Analytics”, IJDEIC, vol. 8, no. 2, pp. 01–18, Jul. 2025, doi: 10.67228/30715717/IJDEIC-2025PII3J2Q.

  • Abstract

    The growing adoption of data-driven decision-making has improved operational intelligence, predictive analytics, and strategic planning across enterprises. However, privacy regulations, data sovereignty requirements, and competitive concerns often restrict direct data sharing between organizations. Federated Data Engineering (FDE) addresses these challenges by enabling collaborative analytics without transferring sensitive raw data. This paper presents a privacy-aware Federated Data Engineering framework that integrates federated learning, distributed data engineering, and secure model aggregation for cross-enterprise analytics. The framework supports decentralized data preprocessing, feature engineering, and encrypted parameter sharing while complying with regulations such as GDPR and HIPAA. It incorporates privacy-enhancing technologies, including differential privacy, secure multi-party computation, homomorphic encryption, and blockchain-based auditing, to ensure confidentiality, integrity, and transparency. The proposed architecture also improves scalability, communication efficiency, fault tolerance, and interoperability across heterogeneous enterprise environments. Experimental evaluation demonstrates enhanced collaborative analytics with reduced privacy risks and communication overhead. The framework provides a practical foundation for secure, trustworthy, and privacy-preserving cross-enterprise data collaboration in healthcare, finance, manufacturing, and other data-intensive industries.

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