Abstract
MapReduce is a powerful model for parallel data processing. The motivation of this work is to allow running map-reduce jobs partially on untrusted infrastructures, such as public clouds and desktop grid, while using a trusted infrastructure, such as private cloud, to ensure that no outsider could get the ’entire’ information. Our idea is to break data into meaningless chunks and spread them on a combination of public and private clouds so that the compromise would not allow the attacker to reconstruct the whole data-set. To realize this, we use the Information Dispersion Algorithms (IDA), which allows to split a file into pieces so that, by carefully dispersing the pieces, there is no method for a single node to reconstruct the data if it cannot collaborate with other nodes. We propose a protocol that allows MapReduce computing nodes to exchange the data and perform IDA-aware MapReduce computation. We conduct experiments on the Grid’5000 testbed and report on performance evaluation of the prototype.
This work is supported by the French Agence Nationale de la Recherche through the MapReduce grant under contract ANR-10-SEGI- 001-01, as well as INRIA ARC BitDew.
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Ben Cheikh, A., Abbes, H., Fedak, G. (2014). Towards Privacy for MapReduce on Hybrid Clouds Using Information Dispersal Algorithm. In: Hameurlain, A., Dang, T.K., Morvan, F. (eds) Data Management in Cloud, Grid and P2P Systems. Globe 2014. Lecture Notes in Computer Science, vol 8648. Springer, Cham. https://doi.org/10.1007/978-3-319-10067-8_4
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DOI: https://doi.org/10.1007/978-3-319-10067-8_4
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