A new bottom-up l-diversity method for Apache spark

This paper addresses the critical issue of preserving privacy in data sharing by introducing 'ImBLd', an enhanced multidimensional bottom-up anonymisation approach based on the l-diversity privacy model. Implemented on the Apache Spark framework, ImBLd optimises data utility through efficient data insertion and splitting processes. Our method integrates data records from distributed workers into an enhanced bottom-up R-tree, significantly minimising data loss. Experimental results demonstrate that ImBLd outperforms traditional top-down methods, achieving superior data utility and reduced execution times. These findings highlight ImBLd's potential for efficient and scalable privacy-preserving data publishing in large-scale datasets.