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Secure Joins with MapReduce

  • Xavier Bultel
  • Radu Ciucanu
  • Matthieu GiraudEmail author
  • Pascal Lafourcade
  • Lihua Ye
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11358)

Abstract

MapReduce is one of the most popular programming paradigms that allows a user to process Big data sets. Our goal is to add privacy guarantees to the two standard algorithms of join computation for MapReduce: the cascade algorithm and the hypercube algorithm. We assume that the data is externalized in an honest-but-curious server and a user is allowed to query the join result. We design, implement, and prove the security of two approaches: (i) Secure-Private, assuming that the public cloud and the user do not collude, (ii) Collision-Resistant-Secure-Private, which resists to collusions between the public cloud and the user i.e., when the public cloud knows the secret key of the user.

Keywords

Database query MapReduce Security Natural joins 

Notes

Acknowledgements

This research was conducted with the support of the FEDER program of 2014–2020, the region council of Auvergne-Rhône-Alpes, the support of the “Digital Trust” Chair from the University of Auvergne Foundation, the Indo-French Centre for the Promotion of Advanced Research (IFCPAR) and the Center Franco-Indien Pour La Promotion De La Recherche Avancée (CEFIPRA) through the project DST/CNRS 2015-03 under DST-INRIA-CNRS Targeted Programme.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xavier Bultel
    • 1
  • Radu Ciucanu
    • 2
  • Matthieu Giraud
    • 3
    Email author
  • Pascal Lafourcade
    • 3
  • Lihua Ye
    • 4
  1. 1.IRISA, Université de Rennes 1RennesFrance
  2. 2.INSA Centre Val de Loire, Univ. Orléans, LIFO EA 4022BourgesFrance
  3. 3.LIMOS, Université Clermont AuvergneClermont-FerrandFrance
  4. 4.Harbin Institute of TechnologyHarbinChina

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