D-Mash: A Framework for Privacy-Preserving Data-as-a-Service Mashups

Mahtab Arafati, Gaby G. Dagher, Benjamin C.M. Fung, Patrick C.K. Hung

Research output: Chapter in Book/Report/Conference proceedingChapter

16 Scopus citations

Abstract

Data-as-a-Service ( DaaS ) mashup enables data providers to dynamically integrate their data on demand depending on consumers’ requests. Utilizing DaaS mashup, however, involves some challenges. Mashing up data from multiple sources to answer a consumer’s request might reveal sensitive information and thereby compromise the privacy of individuals. Moreover, data integration of arbitrary DaaS providers might not always be sufficient to answer incoming requests. In this paper, we provide a cloud-based framework for privacy-preserving DaaS mashup that enables secure collaboration between DaaS providers for the purpose of generating an anonymous dataset to support data mining. Experiments on real-life data demonstrate that our DaaS mashup framework is scalable and can efficiently and effectively satisfy the data privacy and data mining requirements specified by the DaaS providers and the data consumers.
Original languageAmerican English
Title of host publicationProceedings: 2014 IEEE Seventh International Conference on Cloud Computing: CLOUD 2014
DOIs
StatePublished - 2014
Externally publishedYes

Keywords

  • anonymization
  • data mashup
  • data mining
  • data privacy
  • web services

EGS Disciplines

  • Computer Sciences

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