Private and Secure Secret Shared MapReduce

Data outsourcing allows data owners to keep their data in public clouds. However, public clouds do not ensure the privacy of data and computations. One fundamental and useful framework for processing data in a distributed fashion is MapReduce. In this paper, we investigate and present techniques for executing MapReduce computations in the public cloud while preserving privacy. Specifically, we propose a technique to outsource a database using Shamir secret-sharing scheme to the public clouds, and then, provide privacy-preserving algorithms for performing search and fetch, equijoin, and range queries using MapReduce. Consequently, in our proposed algorithms, the public cloud cannot learn the database or the computations. All the proposed algorithms eliminate the role of the database owner, which only creates and distributes secret-shares once, and minimize the role of the user, which only needs to perform a simple operation for reconstructing the result, for query processing. We evaluate the efficiency of all the algorithms by (i) the number of communication rounds (between a user and a cloud), (ii) the total amount of bit flow (between a user and a cloud), and (iii) the computational load at the user-side and the cloud-side.