Bi-level Protected Compressive Sampling

TL;DR

This paper introduces a bi-level protected compressive sampling (BLP-CS) model that enhances security in CS-based encryption, allowing key reuse while resisting common attacks, especially suitable for resource-limited wireless sensors.

Contribution

The paper proposes a novel BLP-CS model utilizing artificial basis mismatch techniques and non-RIP matrices to enable secure, key-reusable compressive sampling.

Findings

Effective in resisting attacks with key reuse

Suitable for resource-limited wireless sensors

Simulation results validate the model's security and practicality

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.