A privacy preserving platform for MapReduce

Sibghat Ullah Bazai*, Julian Jang-Jaccard, Xuyun Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

6 Citations (Scopus)

Abstract

Big data applications typically require a large number of clusters, running in parallel, to process data fast and more efficiently. This is typically controlled and managed by MapReduce. In MapReduce operations, Mappers transform input original key/value pairs to a set of intermediate key/value pairs while Reducers aggregate a set of intermediate values, compute and write to the output. The output however can bring serious privacy concerns. Firstly, the output can directly leak sensitive information because it contains the global view of the final computation. Secondly, the output can also indirectly leak information via composite attacks where the adversary can link it with public information published via different sources such as Facebook or Twitter. To address such privacy concerns, we propose a privacy preserving platform which can prevent privacy leakage in MapReduce. Our platform can be plugged into the Reduce phase to sanitize the final output in such a way that the privacy is preserved while it yet provides a high data utility. We demonstrate the feasibility of our platform by providing empirical studies and highlights that our proposal can be used for real life applications.

Original languageEnglish
Title of host publicationApplications and Techniques in Information Security
Subtitle of host publication8th International Conference, ATIS 2017, Proceedings
EditorsLynn Batten, Dong Seong Kim, Xuyun Zhang, Gang Li
Place of PublicationSingapore
PublisherSpringer, Springer Nature
Pages88-99
Number of pages12
ISBN (Electronic)9789811054211
ISBN (Print)9789811054204
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event8th International Conference on Applications and Techniques in Information Security, ATIS 2017 - Auckland, New Zealand
Duration: 6 Jul 20177 Jul 2017

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer Nature
Volume719
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference8th International Conference on Applications and Techniques in Information Security, ATIS 2017
CountryNew Zealand
CityAuckland
Period6/07/177/07/17

Keywords

  • Differential privacy
  • K-anonymity
  • MapReduce
  • New york taxi data

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