Privacy-by-Design Data Engineering Framework for Multi-Cloud and Hybrid Data Platforms
DOI:
https://doi.org/10.18486/13.3.187Keywords:
Privacy-by-Design, Data Engineering, Multi-Cloud Computing, Hybrid Cloud, Data Governance, Privacy Preservation, Secure Data Analytics, Enterprise Data PlatformsAbstract
Enterprises are heading into multi-cloud and hybrid cloud environments, which have enhanced capabilities for enterprise data management, but have also complicated privacy and security issues, governance models, and inter-cloud communication and regulatory compliance. The paper introduces an Enterprise Data Engineering Architecture for Privacy-by-Design that combines privacy-aware data ingestion, adaptive policy enforcement, secure data transformation, governance management, compliance verification and intelligent resource orchestration into a single framework. The framework is tested for effectiveness against the TPCx-BB (BigBench) benchmark dataset to give it a realistic test with enterprise workloads of structured, semi-structured and unstructured data. Experimental results show that the proposed framework achieves 99.11% of privacy compliance rate, 2.27 s of query execution time, 17.84 GB/s of data processing throughput, 63 ms of access latency, 331 MB of communication overhead, 99.18% of governance consistency, 99.14% of metadata synchronization accuracy, 99.08% of policy enforcement accuracy, 90.8% of resource utilization and 1.00 of scalability compliance index which exceed most of the evaluation metrics used in comparing with existing enterprise data engineering platforms. Moreover, framework shows only 0.46% privacy leakage, 99.24% unauthorized access detection, 99.73% secure data transfer, 6.18% encryption overhead, 99.58% success rate of the audit and 0.37% success rate at policy violations, thus reinforcing its capability of providing secure, privacy-preserving, and scalable enterprise data engineering for enterprise data and services in modern multi-cloud and hybrid cloud environments.
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