MR-RBAT: Anonymizing Large Transaction Datasets Using MapReduce

Conference paper

DOI: 10.1007/978-3-319-20810-7_1

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9149)
Cite this paper as:
Memon N., Shao J. (2015) MR-RBAT: Anonymizing Large Transaction Datasets Using MapReduce. In: Samarati P. (eds) Data and Applications Security and Privacy XXIX. DBSec 2015. Lecture Notes in Computer Science, vol 9149. Springer, Cham


Privacy is a concern when publishing transaction data for applications such as marketing research and biomedical studies. While methods for anonymizing transaction data exist, they are designed to run on a single machine, hence not scalable to large datasets. Recently, MapReduce has emerged as a highly scalable platform for data-intensive applications. In the paper, we consider how MapReduce may be used to provide scalability in transaction anonymization. More specifically, we consider how RBAT may be parallelized using MapReduce. RBAT is a sequential method that has some desirable features for transaction anonymization, but its highly iterative nature makes its parallelization challenging. A direct implementation of RBAT on MapReduce using data partitioning alone can result in significant overhead, which can offset the gains from parallel processing. We propose MR-RBAT that employs two parameters to control parallelization overhead. Our experimental results show that MR-RBAT can scale linearly to large datasets and can retain good data utility.

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  1. 1.School of Computer Science and InformaticsCardiff UniversityCardiffUK

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