Journal
IEEE COMMUNICATIONS LETTERS
Volume 25, Issue 4, Pages 1119-1123Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2020.3044727
Keywords
Servers; Encoding; Complexity theory; Task analysis; Redundancy; Matrix converters; Upper bound; Distributed computing; secure matrix multiplication; grouping; Lagrange encoding
Categories
Funding
- National Natural Science Foundation of China [61871331, 61941106]
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This study focuses on the problem of distributed Secure Batch Matrix Multiplication (SBMM) and presents a computation strategy to characterize the trade-off between recovery threshold, system cost and system complexity, based on grouping Lagrange encoding.
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.
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