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arXiv · 1406.1725

Bi-level Protected Compressive Sampling

Leo Yu Zhang·Kwok-Wo Wong·Yushu Zhang·Jiantao Zhou
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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.

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Leo Yu Zhang, Kwok-Wo Wong, Yushu Zhang, Jiantao Zhou. 2015-08-05. Bi-level Protected Compressive Sampling. https://doi.org/10.1109/tmm.2016.2581593

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