A Communication-Efficient Distributed Matrix Multiplication Scheme with Privacy, Security, and Resiliency

Secure distributed matrix multiplication (SDMM) schemes are crucial for distributed learning algorithms where extensive data computation is distributed across multiple servers. Inspired by the application of repairing Reed-Solomon (RS) codes in distributed storage and secret sharing, we propose SDMM...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2024-08, Vol.26 (9), p.743
Main Authors: Wang, Tao, Shi, Zhiping, Yang, Juan, Liu, Sha
Format: Article
Language:English
Subjects:
Algorithms
Bandwidths
Communication
Data mining
Distributed processing
File servers
Information theory
interleaved codes
Linear codes
Machine learning
Polynomials
Privacy
Reed–Solomon codes
Resilience
secure distributed matrix multiplication
trace polynomials
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Summary:Secure distributed matrix multiplication (SDMM) schemes are crucial for distributed learning algorithms where extensive data computation is distributed across multiple servers. Inspired by the application of repairing Reed-Solomon (RS) codes in distributed storage and secret sharing, we propose SDMM schemes with reduced communication overhead through the use of trace polynomials. Specifically, these schemes are designed to address three critical concerns: (i) ensuring information-theoretic privacy against collusion among servers; (ii) providing security against Byzantine servers; and (iii) offering resiliency against stragglers to mitigate computing delays. To the best of our knowledge, security and resiliency are being considered for the first time within trace polynomial-based approaches. Furthermore, our schemes offer the advantage of reduced sub-packetization and a lower server-count requirement, which diminish the computational complexity and download cost for the user.
ISSN:1099-4300
1099-4300
DOI:10.3390/e26090743