Privacy-Preserving Secret Shared Computations using MapReduce
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Data outsourcing allows data owners to keep their data at untrusted clouds that do not ensure the privacy of data and/or computations. One useful framework for fault-tolerant data processing in a distributed fashion is MapReduce, which was developed for trusted private clouds. This paper presents algorithms for data outsourcing based on Shamir's secret-sharing scheme and for executing privacy-preserving SQL queries such as count, selection including range selection, projection, and join while using MapReduce as an underlying programming model. The proposed algorithms prevent the untrusted cloud to know the database or the query while also preventing output size and access-pattern attacks. Interestingly, our algorithms do not need the database owner, which only creates and distributes secret-shares once, to be involved to answer any query, and hence, the database owner also cannot learn the query. We evaluate the efficiency of the algorithms on parameters: (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.
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