
Adaptive Private Distributed Matrix Multiplication
We consider the problem of designing codes with flexible rate (referred ...
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Secure Private and Adaptive Matrix Multiplication Beyond the Singleton Bound
Consider the problem of designing secure and private codes for distribut...
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Coded Secure MultiParty Computation for Massive Matrices with Adversarial Nodes
In this work, we consider the problem of secure multiparty computation ...
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Erasure coding for distributed matrix multiplication for matrices with bounded entries
Distributed matrix multiplication is widely used in several scientific d...
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Distributed and Private Coded Matrix Computation with Flexible Communication Load
Tensor operations, such as matrix multiplication, are central to larges...
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Private and Secure Distributed Matrix Multiplication with Flexible Communication Load
Large matrix multiplications are central to largescale machine learning...
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Minimizing Latency for Secure Coded Computing Using Secret Sharing via Staircase Codes
We consider the setting of a Master server, M, who possesses confidentia...
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Rateless Codes for Private Distributed MatrixMatrix Multiplication
We consider the problem of designing rateless coded private distributed matrixmatrix multiplication. A master server owns two private matrices A and B and wants to hire worker nodes to help compute the multiplication. The matrices should remain private from the workers, in an informationtheoretic sense. This problem has been considered in the literature and codes with a predesigned threshold are constructed. More precisely, the master assigns tasks to the workers and waits for a predetermined number of workers to finish their assigned tasks. The size of the tasks assigned to the workers depends on the designed threshold. We are interested in settings where the size of the task must be small and independent of the designed threshold. We design a rateless private matrixmatrix multiplications scheme, called RPM3. Our scheme fixes the size of the tasks and allows the master to send multiple tasks to the workers. The master keeps receiving results until it can decode the multiplication. Two main applications require this property: i) leverage the possible heterogeneity in the system and assign more tasks to workers that are faster; and ii) assign tasks adaptively to account for a possibly timevarying system.
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