Towards Privacy for MapReduce on Hybrid Clouds using Information Dispersal Algorighm

Abstract. MapReduce is a powerful model for parallel data processing. The motivation of this work is to allow running map-reduce jobs par-tially on untrusted infrastructures, such as public clouds and desktop grid, while using a trusted infrastructure, such as private cloud, to en-sure 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 compu-tation. We conduct experiments on the Grid’5000 testbed and report on performance evaluation of the prototype.