Multivariate Cryptography Based on Clipped Hopfield Neural Network.

Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in space. The Diffie-Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.

Wave-atoms-based multipurpose scheme via perceptual image hashing and watermarking.

This paper presents a novel multipurpose scheme for content-based image authentication and copyright protection using a perceptual image hashing and watermarking strategy based on a wave atom transform. The wave atom transform is expected to outperform other transforms because it gains sparser expansion and better representation for texture than other traditional transforms, such as wavelet and curvelet transforms. Images are decomposed into multiscale bands with a number of tilings using the wave atom transform. Perceptual hashes are then extracted from the features of tiling in the third scale band for the purpose of content-based authentication; simultaneously, part of the selected hashes are designed as watermarks, which are embedded into the original images for the purpose of copyright protection. The experimental results demonstrate that the proposed scheme shows great performance in content-based authentication by distinguishing the maliciously attacked images from the nonmaliciously attacked images. Moreover, watermarks extracted from the proposed scheme also achieve high robustness against common malicious and nonmalicious image-processing attacks, which provides excellent copyright protection for images.

Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments.

BACKGROUND: Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increasingly popular to solve this type of problems. These models show good performance in accommodating noise, variability and low replication of microarray data. However, the correlation between different fluorescent signals measured from a gene spot is ignored, which can diversely affect the data analysis step. In fact, the intensities of the two signals are significantly correlated across samples. The larger the log-transformed intensities are, the smaller the correlation is. RESULTS: Motivated by the complicated error relations in microarray data, we propose a multivariate hierarchical Bayesian framework for data analysis in the replicated microarray experiments. Gene expression data are modelled by a multivariate normal distribution, parameterized by the corresponding mean vectors and covariance matrixes with a conjugate prior distribution. Within the Bayesian framework, a generalized likelihood ratio test (GLRT) is also developed to infer the gene expression patterns. Simulation studies show that the proposed approach presents better operating characteristics and lower false discovery rate (FDR) than existing methods, especially when the correlation coefficient is large. The approach is illustrated with two examples of microarray analysis. The proposed method successfully detects significant genes closely related to the experimental states, which are verified by the biological information. CONCLUSIONS: The multivariate Bayesian model, compatible with the dependence between mean and variance in the univariate Bayesian model, relaxes the constant coefficient of variation assumption between measurements by adding a covariance structure. This model improves the identification of differentially expressed genes significantly since the Bayesian model fit well with the microarray data.