Secure and Verifiable Outsourcing Algorithm for Large-Scale Matrix Multiplication on Public Cloud Server

2018 ◽  
pp. 575-586 ◽  
Author(s):  
Malay Kumar ◽  
Manu Vardhan
Keyword(s):  
Large Scale ◽  
Matrix Multiplication ◽  
Public Cloud ◽  
Cloud Server ◽  
Outsourcing Algorithm ◽  
Scale Matrix

A novel publicly delegable secure outsourcing algorithm for large-scale matrix multiplication

2020 ◽  
Vol 38 (5) ◽  
pp. 6445-6455
Author(s):  
Malay Kumar ◽  
Vaibhav Mishra ◽  
Anurag Shukla ◽  
Munendra Singh ◽  
Manu Vardhan
Keyword(s):  
Large Scale ◽  
Matrix Multiplication ◽  
Secure Outsourcing ◽  
Outsourcing Algorithm ◽  
Scale Matrix

scholarly journals Efficient Noninteractive Outsourcing of Large-Scale QR and LU Factorizations

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Lingzan Yu ◽  
Yanli Ren ◽  
Guorui Feng ◽  
Xinpeng Zhang
Keyword(s):  
Large Scale ◽  
Sparse Matrix ◽  
Sparse Matrices ◽  
Data Outsourcing ◽  
Mathematical Methods ◽  
Original Matrix ◽  
Computational Overhead ◽  
Cloud Server ◽  
Outsourcing Algorithm ◽  
Scale Matrix

QR and LU factorizations are two basic mathematical methods for decomposition and dimensionality reduction of large-scale matrices. However, they are too complicated to be executed for a limited client because of big data. Outsourcing computation allows a client to delegate the tasks to a cloud server with powerful resources and therefore greatly reduces the client’s computation cost. However, the previous methods of QR and LU outsourcing factorizations need multiple interactions between the client and cloud server or have low accuracy and efficiency in large-scale matrix applications. In this paper, we propose a noninteractive and efficient outsourcing algorithm of large-scale QR and LU factorizations. The proposed scheme is based on the specific perturbation method including a series of consecutive and sparse matrices, which can be used to protect the original matrix and obtain the results of factorizations. The generation and inversion of sparse matrix has small workloads on the client’s side, and the communication cost is also small since the client does not need to interact with the cloud server in the outsourcing algorithms. Moreover, the client can verify the outsourcing result with a probability of approximated to 1. The experimental results manifest that as for the client, the proposed algorithms reduce the computational overhead of direct computation successfully, and it is most efficient compare with the previous ones.

scholarly journals Towards a Multi-array Architecture for Accelerating Large-scale Matrix Multiplication on FPGAs

2018 ◽  
Author(s):  
Junzhong Shen ◽  
Yuran Qiao ◽  
You Huang ◽  
Mei Wen ◽  
Chunyuan Zhang
Keyword(s):  
Large Scale ◽  
Matrix Multiplication ◽  
Array Architecture ◽  
Scale Matrix

On Large-Scale Matrix-Matrix Multiplication On Compressed Structures

2021 ◽  
Author(s):  
Sudhindra Gopal Krishna ◽  
Aditya Narasimhan ◽  
Sridhar Radhakrishnan ◽  
Richard Veras
Keyword(s):  
Large Scale ◽  
Matrix Multiplication ◽  
Scale Matrix

scholarly journals Outsourcing Computing of Large Matrix Jordan Decomposition

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Hongfeng Wu ◽  
Jingjing Yan
Keyword(s):  
Large Scale ◽  
Security Analysis ◽  
Sensitive Information ◽  
Jordan Decomposition ◽  
Verification Algorithm ◽  
Resource Limited ◽  
Large Matrix ◽  
Cloud Server ◽  
Scale Matrix ◽  
Efficient Verification

The Jordan decomposition of matrix is a typical scientific and engineering computational task, but such computation involves enormous computing resources for large matrices, which is burdensome for the resource-limited clients. Cloud computing enables computational resource-limited clients to economically outsource such problems to the cloud server. However, outsourcing Jordan decomposition of large-scale matrix to the cloud brings great security concerns and challenges since the matrices usually contain sensitive information. In this paper, we present a secure, verifiable, efficient, and privacy preserving algorithm for outsourcing Jordan decomposition of large-scale matrix. Security analysis shows that our algorithm is practically secure. Efficient verification algorithm is used to verify the results returned from the cloud.

A chain-multiplier for large scale matrix multiplication

2017 ◽  
Author(s):  
Can Wei ◽  
Yukun Song ◽  
Duoli Zhang
Keyword(s):  
Large Scale ◽  
Matrix Multiplication ◽  
A Chain ◽  
Scale Matrix

scholarly journals Secure and Efficient Protocol for Outsourcing Large-Scale Matrix Multiplication to the Cloud

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 227556-227565
Author(s):  
Yu Wu ◽  
Yongjian Liao ◽  
Yikuan Liang ◽  
Yulu Liu
Keyword(s):  
Large Scale ◽  
Matrix Multiplication ◽  
Scale Matrix

scholarly journals Privacy preserving, verifiable and efficient outsourcing algorithm for matrix multiplication to a malicious cloud server

2017 ◽  
Vol 4 (1) ◽  
pp. 1295783 ◽  
Author(s):  
Malay Kumar ◽  
Jasraj Meena ◽  
Manu Vardhan ◽  
Robert J. Adams
Keyword(s):  
Matrix Multiplication ◽  
Privacy Preserving ◽  
Cloud Server ◽  
Outsourcing Algorithm

An FPGA-based efficient architecture for accelerating large-scale matrix multiplication

2021 ◽  
Author(s):  
Ahui Huang ◽  
Longlong Zhang ◽  
Jian Long ◽  
Xuan Gao ◽  
Yuanxi Peng
Keyword(s):  
Large Scale ◽  
Matrix Multiplication ◽  
Scale Matrix
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