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Secure Batch Matrix Multiplication From Grouping Lagrange Encoding
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DOI：10.1109/LCOMM.2020.3044727.png)
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Abstract
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In this letter, the problem of distributed Secure Batch Matrix Multiplication (SBMM) is studied, where a user wishes to compute the pairwise products of two batches of massive matrices A and B generated by two external source nodes, with the aid of N distributed servers. The security for data matrices A (resp. B) is guaranteed against any group of up to X-A (resp. X-B) colluding servers. As a result, a computation strategy is presented to characterize the trade-off between recovery threshold, system cost and system complexity, based on grouping Lagrange encoding, which unifies and improves the previous strategies for SBMM.
Keywords:
Servers
Encoding
Complexity theory
Task analysis
Redundancy
Matrix converters
Upper bound
Distributed computing
secure matrix multiplication
grouping
Lagrange encoding
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