A Bottom-up k-anonymization approach for big data publishing

Abstract

As governments and other organizations share larger datasets, keeping individual information private has become increasingly difficult to solve. When publishing the data, data anonymization models like k-anonymity and l-diversity are employed to ensure the trade-off between privacy and data utility. This paper presents a method called Bottom-Up k-anonymization (BU-K), implemented on Apache Spark. It improves efficiency by applying the Bottom-Up Generalization (BUG) approach. BU-KC performs better than Top-Down Specialization (TDS) in terms of scalability, and data privacy, while still keeping the data useful. Moreover, using Apache Spark’s distributed computing architecture significantly improves processing time compared to traditional MapReduce approaches. This work fills a gap in distributed anonymization on Spark by offering a new, efficient, and scalable solution