Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702004
Title: Anonymizing large transaction data using MapReduce
Author: Memon, Neelam
ISNI:       0000 0004 5994 5674
Awarding Body: Cardiff University
Current Institution: Cardiff University
Date of Award: 2016
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Abstract:
Publishing transaction data is important to applications such as marketing research and biomedical studies. Privacy is a concern when publishing such data since they often contain person-specific sensitive information. To address this problem, different data anonymization methods have been proposed. These methods have focused on protecting the associated individuals from different types of privacy leaks as well as preserving utility of the original data. But all these methods are sequential and are designed to process data on a single machine, hence not scalable to large datasets. Recently, MapReduce has emerged as a highly scalable platform for data-intensive applications. In this work, we consider how MapReduce may be used to provide scalability in large transaction data anonymization. More specifically, we consider how setbased generalization methods such as RBAT (Rule-Based Anonymization of Transaction data) may be parallelized using MapReduce. Set-based generalization methods have some desirable features for transaction anonymization, but their highly iterative nature makes parallelization challenging. RBAT is a good representative of such methods. We propose a method for transaction data partitioning and representation. We also present two MapReduce-based parallelizations of RBAT. Our methods ensure scalability when the number of transaction records and domain of items are large. Our preliminary results show that a direct parallelization of RBAT by partitioning data alone can result in significant overhead, which can offset the gains from parallel processing. We propose MR-RBAT that generalizes our direct parallel method and allows to control parallelization overhead. Our experimental results show that MR-RBAT can scale linearly to large datasets and to the available resources while retaining good data utility.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.702004  DOI: Not available
Keywords: QA75 Electronic computers. Computer science
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