Experimental demonstration of synchronous privacy enhanced chaotic temporal phase en/decryption for high speed secure optical communication

Exploiting high-quality reconstruction image encryption strategy by optimized orthogonal compressive sensing

Compressive sensing is favored because it breaks through the constraints of Nyquist sampling law in signal reconstruction. However, the security defects of joint compression encryption and the problem of low quality of reconstructed image restoration need to be solved urgently. In view of this, this paper proposes a compressive sensing image encryption scheme based on optimized orthogonal measurement matrix. Utilizing a combination of DWT and OMP, along with chaos, the proposed scheme achieves high-security image encryption and superior quality in decryption reconstruction. Firstly, the orthogonal optimization method is used to improve the chaotic measurement matrix. Combined with Part Hadamard matrix, the measurement matrix with strong orthogonal characteristics is constructed by Kronecker product. Secondly, the original image is sparsely represented by DWT. Meanwhile, Arnold scrambling is used to disturb the correlation between its adjacent pixels. Following this, the image is compressed and measured in accordance with the principles of compressive sensing and obtain the intermediate image to be encrypted. Finally, the chaotic sequence generated based on 2D-LSCM is used to perform on odd-even interleaved diffusion and row-column permutation at bit-level to obtain the final ciphertext. The experimental results show that this scheme meets the cryptographic requirements of obfuscation, diffusion and avalanche effects, and also has a large key space, which is sufficient to resist brute-force cracking attacks. Based on the sparse and reconstruction algorithm of compressive sensing proposed in this paper, it has better image restoration quality than similar algorithms. Consequently, the compressive sensing image encryption scheme enhances both security and reconstruction quality, presenting promising applications in the evolving landscape of privacy protection for network big data.

Secure image encryption algorithm using chaos-based block permutation and weighted bit planes chain diffusion

Aiming at the problem of insufficient security of image encryption technology, a secure image encryption algorithm using chaos-based block permutation and weighted bit planes chain diffusion is proposed, which is based on a variant structure of classical permutation-diffusion. During the permutation phase, the encryption operations of dividing an image into sub-block, block scrambling, block rotation and block inversion, negative-positive transformation, color component shuffling are performed sequentially with chaotic sequences of plaintext association. In the chain diffusion stage, different encryption strategies are adopted for the high and low 4-bit planes according to the weight of image information. Theoretical analyses and empirical results substantiate that the algorithm conforms to the cryptographic requirements of confusion, diffusion, and avalanche effects, while possessing excellent numerical statistical properties with a large cryptographic space. Therefore, the cryptanalysis-propelled security enhancement mechanism proposed in this paper effectively amplifies the aptitude of the algorithm to withstand cryptographic attacks.

High-speed chaos-based secure optical communications over 130-km multi-core fiber

Chaotic optical communication is of great significance for secure data transmission. Despite rapid development over the decades, high-speed (>100 Gbps) and long-distance (>100 km) chaotic optical communication in a single fiber is still full of challenges. Here, we propose and experimentally demonstrate high-speed and long-distance chaos-based secure optical communications using mutual injection of semiconductor lasers and space-division multiplexing (SDM) techniques. The encrypted signals are transmitted through all seven core channels of the multi-core fiber (MCF), which effectively expands the aggregate transmission capacity of a single fiber. A pair of source and synchronization devices based on mutual injection of semiconductor lasers are employed to effectively encrypt and decrypt signals. Chaos-based secure optical communications with 70-Gbps on-off keying (OOK) and 140-Gbps quadrature phase-shift keying (QPSK) signals over a 130-km MCF are successfully demonstrated in the experiment with favorable performance. The demonstration may pave the way for future ultrahigh capacity and ultra-long distance chaotic optical communications by fully exploiting multi-dimensional resources of light waves, including the spatial dimension.

Physical-layer security of optical communication based on chaotic optical encryption without an additional driving signal

We propose and numerically demonstrate a scheme for physical-layer security based on chaotic phase encryption, where the transmitted carrier signal is used as the common injection for chaos synchronization, so there is no need for additional common driving. To ensure privacy, two identical optical scramblers consisting of a semiconductor laser and a dispersion component are used to observe the carrier signal. The results show that the responses of the optical scramblers are highly synchronized but are not synchronized with the injection. By properly setting the phase encryption index, the original message can be well encrypted and decrypted. Moreover, the legal decryption performance is sensitive to the parameter mismatch, since it can degrade the synchronization quality. A slight drop in synchronization induces an evident deterioration in decryption performance. Therefore, without perfectly reconstructing the optical scrambler, the original message cannot be decoded by an eavesdropper.

Modeling of a multi-parameter chaotic optoelectronic oscillator based on the Fourier neural operator

A model construction scheme of chaotic optoelectronic oscillator (OEO) based on the Fourier neural operator (FNO) is proposed. Different from the conventional methods, we learn the nonlinear dynamics of OEO (actual components) in a data-driven way, expecting to obtain a multi-parameter OEO model for generating chaotic carrier with high-efficiency and low-cost. FNO is a deep learning architecture which utilizes neural network as a parameter structure to learn the trajectory of the family of equations from training data. With the assistance of FNO, the nonlinear dynamics of OEO characterized by differential delay equation can be modeled easily. In this work, the maximal Lyapunov exponent is applied to judge whether these time series have chaotic behavior, and the Pearson correlation coefficient (PCC) is introduced to evaluate the modeling performance. Compare with long and short-term memory (LSTM), FNO is not only superior to LSTM in modeling accuracy, but also requires less training data. Subsequently, we analyze the modeling performance of FNO under different feedback gains and time delays. Both numerical and experimental results show that the PCC can be greater than 0.99 in the case of low feedback gain. Next, we further analyze the influence of different system oscillation frequencies, and the generalization ability of FNO is also analyzed.