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.
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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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Bultel, X., Ciucanu, R., Giraud, M., Lafourcade, P., Ye, L. (2019). Secure Joins with MapReduce. In: Zincir-Heywood, N., Bonfante, G., Debbabi, M., Garcia-Alfaro, J. (eds) Foundations and Practice of Security. FPS 2018. Lecture Notes in Computer Science(), vol 11358. Springer, Cham. https://doi.org/10.1007/978-3-030-18419-3_6
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