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.Comment: 14 pages, 8 figure

Compressed sensing for electrocardiogram acquisition in wireless body sensor network: A comparative analysis

“…Data records presented in this article are expected serving as the referenced support for practical applications. Besides, state-of-the-art achievement reveals that CS also has certain cryptographic properties; 9,27 thus, it can further be exploited as a encryption scheme for the acquired data stream and indicates possible saving of the Advanced Encryption Standard (AES) encryption in WBSN protocols (generally in the Mac layer of IEEE 802.15.4/802.15.6/Bluetooth). In literatures, achievements in this kind have drawn increasing attention.…”

“…However, this asymmetry of complexity, fortunately, matches practical applications where signal acquisition devices are generally resource constrained but the reconstruction operations are usually implemented on powerful servers. 9 The WBSN is indeed a typical case. The biosensors have limited energy, computation, memory, and storage; meanwhile, they are required to perform longterm signal acquisition and wireless communication.…”

Secure Wireless Communications Based on Compressive Sensing: A Survey

“…In general, the measurement matrix needs to be updated once to ensure the security of CSC, since the attacker can launch a plaintext attack to reveal the measurement matrix by possibly contrasting several plaintext and ciphertext pairs. However, a key reuse circumstance of utilizing a fixed measurement matrix with multiple times was devised in [58]. Except the secret protection of the measurement matrix, a secret sparsifying basis B can be added in the signal sampling, thus it becomes a bi-level protection sampling model:…”

“…Generally speaking, the measurement matrix needs to be updated once to be against common attacks. For the purpose of using a fixed measurement matrix repeatedly, a bilevel protected CS scheme was suggested in [58], whose basic idea is to transfer the sparsifying basis from the sensing matrix to the measurement matrix and then set it a key. The transfer was verified in the sense that the reconstruction performance will not be affected.…”

Steerable Discrete Fourier Transform

“…Given θ, it is possible to reconstruct x and then we can obtain the original signal x by applying the inverse SDFT. We can also consider the SDFT as a keyed transform basis that can be used for compressed sensing-based cryptography [31,32].…”