TY - GEN
T1 - Secure parallel Outsourcing Scheme for Large-scale Matrix Multiplication on Distributed Cloud Servers
AU - Wang, Yinlong
AU - Tao, Yunting
AU - Kong, Fanyu
AU - Gu, Zhaoquan
AU - Yu, Jia
AU - Zhang, Hanlin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Large-scale matrix multiplication is a computational bottleneck in various applications including artificial intelligence and machine learning. Given the time complexity of O(n3) for matrix multiplication, large matrix computation is exceedingly time-consuming for the client-side user. By outsourcing this task to cloud servers with substantial computational resources, we can significantly reduce the client-side computational time. This paper presents a parallel matrix multiplication outsourcing scheme based on Cannon's algorithm. By distributing the matrix across multiple cloud servers for parallel computation, we can get a significant efficiency speedup. Our scheme employs multiple cloud servers to perform parallel matrix computation, reducing the computational load by 89-97% when utilizing 4-16 servers as opposed to using a single server. We provide a comprehensive analysis of the scheme's correctness, security, and verifiability, substantiating the benefits of our approach through the experimental data.
AB - Large-scale matrix multiplication is a computational bottleneck in various applications including artificial intelligence and machine learning. Given the time complexity of O(n3) for matrix multiplication, large matrix computation is exceedingly time-consuming for the client-side user. By outsourcing this task to cloud servers with substantial computational resources, we can significantly reduce the client-side computational time. This paper presents a parallel matrix multiplication outsourcing scheme based on Cannon's algorithm. By distributing the matrix across multiple cloud servers for parallel computation, we can get a significant efficiency speedup. Our scheme employs multiple cloud servers to perform parallel matrix computation, reducing the computational load by 89-97% when utilizing 4-16 servers as opposed to using a single server. We provide a comprehensive analysis of the scheme's correctness, security, and verifiability, substantiating the benefits of our approach through the experimental data.
KW - Cloud computing
KW - Data privacy
KW - Matrix multiplication
KW - Parallel outsourcing
UR - https://www.scopus.com/pages/publications/85190260443
U2 - 10.1109/ICPADS60453.2023.00337
DO - 10.1109/ICPADS60453.2023.00337
M3 - 会议稿件
AN - SCOPUS:85190260443
T3 - Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS
SP - 2531
EP - 2538
BT - Proceedings - 2023 IEEE 29th International Conference on Parallel and Distributed Systems, ICPADS 2023
PB - IEEE Computer Society
T2 - 29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023
Y2 - 17 December 2023 through 21 December 2023
ER -