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• W4289713085 abstract "We consider the problem of designing a coding scheme that allows both sparsity and privacy for distributed matrix-vector multiplication. Perfect information-theoretic privacy requires encoding the input sparse matrices into matrices distributed uniformly at random from the considered alphabet; thus destroying the sparsity. Computing matrix-vector multiplication for sparse matrices is known to be fast. Distributing the computation over the non-sparse encoded matrices maintains privacy, but introduces artificial computing delays. In this work, we relax the privacy constraint and show that a certain level of sparsity can be maintained in the encoded matrices. We consider the chief/worker setting while assuming the presence of two clusters of workers: one is completely untrusted in which all workers collude to eavesdrop on the input matrix and in which perfect privacy must be satisfied; the other is partly trusted, only up to z workers may collude and to which revealing small amount of information about the input matrix is allowed. We design a scheme that trades sparsity for privacy while achieving the desired constraints. We use cyclic task assignments of the encoded matrices to tolerate partial and full stragglers." @default.
• W4289713085 created "2022-08-04" @default.
• W4289713085 creator A5024768934 @default.
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• W4289713085 date "2022-06-26" @default.
• W4289713085 modified "2023-10-05" @default.
• W4289713085 title "Distributed Matrix-Vector Multiplication with Sparsity and Privacy Guarantees" @default.
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• W4289713085 doi "https://doi.org/10.1109/isit50566.2022.9834805" @default.
• W4289713085 hasPublicationYear "2022" @default.
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