Abstract
MapReduce is one of the most popular distributed programming paradigms that allows processing big data sets in parallel on a cluster. MapReduce users often outsource data and computations to a public cloud, which yields inherent security concerns. In this paper, we consider the problem of matrix multiplication and one of the most efficient matrix multiplication algorithms: the Strassen-Winograd (\(\text {SW} \)) algorithm. Our first contribution is a distributed MapReduce algorithm based on \(\text {SW} \). Then, we tackle the security concerns that occur when outsourcing matrix multiplication computation to a honest-but-curious cloud i.e., that executes tasks dutifully, but tries to learn as much information as possible. Our main contribution is a secure distributed MapReduce algorithm called \(\mathrm {S2M3} \) (Secure Strassen-Winograd Matrix Multiplication with MapReduce) that enjoys security guarantees such as: none of the cloud nodes can learn the input or the output data. We formally prove the security properties of \(\mathrm {S2M3} \) and we present an empirical evaluation devoted to show its efficiency.
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Ciucanu, R., Giraud, M., Lafourcade, P., Ye, L. (2020). Secure and Efficient Matrix Multiplication with MapReduce. In: Obaidat, M. (eds) E-Business and Telecommunications. ICETE 2019. Communications in Computer and Information Science, vol 1247. Springer, Cham. https://doi.org/10.1007/978-3-030-52686-3_6
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