Hybrid Framework: Balancing Data Utility in Privacy-Preserving Big Data Processing (PPBDP)

Big data is a rapidly growing form of information, leading to significant concerns about data privacy. Conventional privacy-preserving methodologies, including anonymization and encryption, exhibit inherent constraints in optimizing the trade-off between data utility and privacy. This study introduces an advanced hybrid privacy preservation framework for big data dissemination, integrating anonymization methodologies to provide comprehensive privacy protection. The proposed framework rigorously maintains the inherent data architecture while simultaneously safeguarding the information’s confidentiality. This methodology is especially pertinent for domains where the integrity and privacy of data are paramount, including health-care, finance, and governmental operations. The proposed model aspires to augment data privacy while concurrently preserving its applicability for analytical and research endeavours.