Hybrid Encryption for Fortifying HDFS Data‎

In the big data era, standard encryption methods alone are not suitable for handling massive, high-velocity data, which negatively impacts the performance of a distributed framework. This paper ‎proposes a hybrid encryption (HE) method that integrates the strengths of the two symmetric algo‎rithms (Twofish-256, AES-256) with the Hadoop Map-Reduce framework (MRF) to fortify Hadoop Distributed File System (HDFS) data. This paper offers dual-level encryption (Twofish -> ‎AES) to mitigate the vulnerabilities of standalone encryption while maintaining optimal perfor‎mance. The experiments on datasets from 32-256 MB show encryption speed improvement of over ‎‎5-6%, efficiency gain of over 5%, and throughput of over 6% compared to hybrid approaches such ‎as CP-ABE+AES, AES+RSA, and standalone encryption schemes AES and Twofish. Additionally, ‎the ANOVA test based on encryption and decryption time gives (F = 2.67, p = 0.07) and (F = 9.9, ‎p = 0.0003) outcomes, which show that the proposed HE approach is highly significant in big data ‎environments. Our novel approach balances security and performance, addresses the weaknesses of ‎individual and hybrid encryption algorithms, ensures compatibility in distributed environments, and ‎complies with data protection regulations. This suggested HE approach (Twofish -> AES) complies ‎with GDPR, HIPAA, and PCI-DSS through key management and resistance to side-channel ‎attacks. The results show feasibility in the government and healthcare sectors, where data protection ‎and large dataset processing are critical‎.