PraDa: Privacy-preserving data-deduplication-as-a-service

Boxiang Dong, Ruilin Liu, Wendy Hui Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

The data-cleaning-as-a-service (DCaS) paradigm enables users to outsource their data and data cleaning needs to computationally powerful third-party service providers. It raises several security issues. One of the issues is how the client can protect the private information in the outsourced data. In this paper, we focus on data deduplication as the main data cleaning task, and design two efficient privacy-preserving data-deduplication methods for the DCaS paradigm. We analyze the robustness of our two methods against the attacks that exploit the auxiliary frequency distribution and the knowledge of the encoding algorithms. Our empirical study demonstrates the efficiency and effectiveness of our privacy preserving approaches.

Original languageEnglish
Title of host publicationCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery, Inc
Pages1559-1568
Number of pages10
ISBN (Electronic)9781450325981
DOIs
StatePublished - 3 Nov 2014
Event23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China
Duration: 3 Nov 20147 Nov 2014

Publication series

NameCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management

Other

Other23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
CountryChina
CityShanghai
Period3/11/147/11/14

Keywords

  • Data deduplication
  • Data-cleaning-as-a-service
  • Outsourcing
  • Privacy-preserving
  • Security

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    Dong, B., Liu, R., & Wang, W. H. (2014). PraDa: Privacy-preserving data-deduplication-as-a-service. In CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management (pp. 1559-1568). (CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management). Association for Computing Machinery, Inc. https://doi.org/10.1145/2661829.2661863