A Novel Privacy-Preserving Framework for Real-Time Big Data Analytics in Heterogeneous Environments

The rapid growth of big data has had a profound impact on decision-making processes across various industries. However, this development has concomitantly given rise to significant privacy concerns, particularly in the context of real-time and heterogeneous data environments. Existing privacy-preserving techniques frequently encounter challenges such as high dimensionality, data diversity, and real-time processing constraints. This paper proposes a novel privacy-preserving framework designed specifically for real-time big data analytics in heterogeneous environments. The proposed framework integrates enhanced k-anonymity with a dynamic generalization algorithm, enabling adaptation to real-time data streams while ensuring privacy. Furthermore, a differential privacy module is incorporated to safeguard sensitive information against re-identification risks in both structured and unstructured datasets. The effectiveness of the framework is evaluated through extensive experimentation on both synthetic and real-world datasets, demonstrating superior scalability, privacy preservation and analytical accuracy in comparison to existing methods. This research addresses critical gaps in current privacy-preserving big data analytics techniques, offering a viable solution for industries and governments requiring secure and efficient real-time data analytics.