Optimized Privacy Preserving Big Data Publishing Approach
Salheddine Kabou, Imad Eddine Kimi, Arbia Boudaouad · Computer Science Journal of Moldova · 2026
In the context of big data, balancing individual privacy with the need for data utility remains a critical challenge. This research presents the Bottom-Up k-Concealment (BU-KC) framework, a pioneering solution for Privacy-Preserving Big Data Publishing (PPBDP). At its core, the k-concealment model addresses the limitations of traditional k-anonymity by minimizing excessive generalizations, ensuring stronger privacy protection while preserving data utility. Combined with the Bottom-Up Generalization (BUG) strategy, BU-KC demonstrates superior performance compared to Top-Down Specialization (TDS) in terms of computational efficiency, scalability, and privacy preservation, while concurrently sustaining data utility. Furthermore, the integration of Apache Spark's distributed computing paradigm enables us to effectively mitigate scalability constraints and processing bottlenecks commonly observed in the anonymization of large-scale datasets.