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Updated: Jun 12, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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

Tao Wang1, Zhiping Shi1, Juan Yang2,3

  • 1National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.

Entropy (Basel, Switzerland)
|September 27, 2024
PubMed
Summary
This summary is machine-generated.

We introduce novel secure distributed matrix multiplication (SDMM) schemes using trace polynomials. These schemes enhance privacy, security, and resiliency while reducing communication overhead for distributed learning.

Keywords:
Reed–Solomon codesinterleaved codessecure distributed matrix multiplicationtrace polynomials

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Area of Science:

  • Cryptography and Distributed Systems
  • Information Theory and Coding

Background:

  • Secure Distributed Matrix Multiplication (SDMM) is vital for distributed learning algorithms.
  • Existing schemes face challenges in privacy, security, and straggler resiliency.
  • Reed-Solomon (RS) codes offer insights for data repair and secret sharing.

Purpose of the Study:

  • To propose novel SDMM schemes leveraging trace polynomials.
  • To enhance information-theoretic privacy against server collusion.
  • To provide security against Byzantine servers and resiliency against stragglers.

Main Methods:

  • Utilizing trace polynomials for SDMM scheme design.
  • Adapting principles from Reed-Solomon (RS) code repair.
  • Developing schemes to address privacy, Byzantine, and straggler issues.

Main Results:

  • Achieved reduced communication overhead compared to existing methods.
  • Introduced schemes offering information-theoretic privacy.
  • Provided security against Byzantine servers and resiliency against stragglers.
  • Demonstrated reduced sub-packetization and lower server-count requirements.

Conclusions:

  • The proposed trace polynomial-based SDMM schemes offer a comprehensive solution for secure and efficient distributed learning.
  • These schemes significantly reduce communication overhead, computational complexity, and download costs.
  • This work pioneers the integration of security and resiliency in trace polynomial-based SDMM.