Computationally Efficient and Secure Kronecker-based Compressive Sensing

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

Click here to download the PDF file.

Creator: 

Firoozi Shahmirzadi, Parichehreh

Date: 

2021

Abstract: 

We propose an efficient permuted Kronecker-based sparse measurement matrix for compressive sensing applications. We use sub-matrices to create a block-diagonal matrix and multiply it with a deterministic permutation matrix to measure the sparse or compressible signals. Using ECG signals from the MIT-BIH Arrhythmia database, we show that the reconstructed signal quality is comparable to the ones achieved using standard compressive sensing methods. Our methodology results in an overall reduction in storage and computations and can be generalized to other classes of eligible measurement matrices in compressive sensing. We show that with the use of a securely generated one-time sensing matrix, our proposed method is computationally secure against plaintext and ciphertext-only attacks. The proposed one-time sensing matrix is superior to other measurement matrices in the literature in terms of the number of linear feedback shift register bits required for their generation.

Subject: 

Education

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Applied Science: 
M.App.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Engineering, Electrical and Computer

Parent Collection: 

Theses and Dissertations

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).