MR-RBAT: Anonymizing Large Transaction Datasets Using MapReduce - Archive ouverte HAL Access content directly
Conference Papers Year : 2015

MR-RBAT: Anonymizing Large Transaction Datasets Using MapReduce

(1) , (1)
1
Neelam Memon
  • Function : Author
  • PersonId : 1029884
Jianhua Shao
  • Function : Author
  • PersonId : 1029885

Abstract

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.
Fichier principal
Vignette du fichier
340025_1_En_1_Chapter.pdf (373.53 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01745812 , version 1 (28-03-2018)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Neelam Memon, Jianhua Shao. MR-RBAT: Anonymizing Large Transaction Datasets Using MapReduce. 29th IFIP Annual Conference on Data and Applications Security and Privacy (DBSEC), Jul 2015, Fairfax, VA, United States. pp.3-18, ⟨10.1007/978-3-319-20810-7_1⟩. ⟨hal-01745812⟩
34 View
68 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More