Bi-level protected compressive sampling

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Leo Yu Zhang
  • Kwok-Wo Wong
  • Yushu Zhang
  • Jiantao Zhou

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number7492261
Pages (from-to)1720-1732
Journal / PublicationIEEE Transactions on Multimedia
Volume18
Issue number9
Online published15 Jun 2016
Publication statusPublished - Sep 2016

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 when 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 measurement matrix construction without restricted isometry property (RIP). 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 reused.

Research Area(s)

  • Compressive sampling (CS), encryption, known/chosen-plaintext attack, random projection, restricted isometry property (RIP)

Citation Format(s)

Bi-level protected compressive sampling. / Zhang, Leo Yu; Wong, Kwok-Wo; Zhang, Yushu; Zhou, Jiantao.

In: IEEE Transactions on Multimedia, Vol. 18, No. 9, 7492261, 09.2016, p. 1720-1732.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review