Bi-level Protected Compressive Sampling

Abstract

Some pioneering works have investigated embedding cryptographic properties in compressive sampling (CS) in a way similar to one-time pad symmetric cipher. This paper tackles the problem of constructing a CS-based symmetric cipher under the key reuse circumstance, i.e., the cipher is resistant to common attacks even a fixed measurement matrix is used multiple times. To this end, we suggest a bi-level protected CS (BLP-CS) model which makes use of the advantage of the non-RIP measurement matrix construction. Specifically, two kinds of artificial basis mismatch techniques are investigated to construct key-related sparsifying bases. It is demonstrated that the encoding process of BLP-CS is simply a random linear projection, which is the same as the basic CS model. However, decoding the linear measurements requires knowledge of both the key-dependent sensing matrix and its sparsifying basis. The proposed model is exemplified by sampling images as a joint data acquisition and protection layer for resource-limited wireless sensors. Simulation results and numerical analyses have justified that the new model can be applied in circumstances where the measurement matrix can be re-used.

14 pages, 8 figures

References in corpus (7)

• Low-complexity Multiclass Encryption by Compressed Sensing
• On Known-Plaintext Attacks to a Compressed Sensing-based Encryption: A Quantitative Analysis
• Permutation Meets Parallel Compressed Sensing: How to Relax Restricted Isometry Property for 2D Sparse Signals
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Cited by in corpus (5)

• Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction
• Steerable Discrete Fourier Transform
• Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG
• Privacy-Assured Outsourcing of Compressed Sensing Reconstruction Service in Cloud
• Secure and efficient compressed sensing based encryption with sparse matrices