Cryptanalysis of enhancement on “quantum blind signature based on two-state vector formalism”

Recently, Yang et al. (Quantum Inf Process 12(1):109, 2013) proposed an enhanced quantum blind signature based on two-taste vector formalism. The protocol can prevent signatory Bob from deriving Alice’s message with invisible photon eavesdropping attack or fake photon attack. In this paper, we show that the enhanced protocol also has a loophole that Alice can utilize an entanglement swapping attack to obtain Bob’s secret key and forge Bob’s valid signature at will later. Then, we reanalyze two existing protocols and try to find some further methods to fix them.     

Comment on “The enhanced quantum blind signature protocol”

Recently, Yang et al. (Quantum Inf Process 12:109–117, 2013) proposed an enhancement on a quantum blind signature based on the two-state vector formalism, afterward a special attack strategy on Yang et al.’s enhanced scheme is put forward, in which the dishonest signer can illegally reveal 25 % of the message of the blind signature requester, but an effective solution has not been presented in their paper. In this paper, we further analyze Yang el al.’s enhanced scheme and find that there is another potential loophole which the blind signature requester can forge the message signer’s signature. Then, an improvement scheme is proposed. Finally, analysis results show that our improved scheme can withstand the blind signature requester’s forgery attack and the above special attack strategy, and our quantum efficiency will still be the same as the primary scheme.     

Identity-based signature scheme based on quadratic residues

Identity-based(ID-based)cryptography has drawn great concerns in recent years,and most of ID-based schemes are constructed from bilinear parings.Therefore,ID-based scheme without pairing is of great interest in the field of cryptography.Up to now, there still remains a challenge to construct ID-based signature scheme from quadratic residues.Thus,we aim to meet this challenge by proposing a concrete scheme.In this paper,we first introduce the technique of how to calculate a 2lth root of a quadratic residue,and then give a concrete ID-based signature scheme using such technique. We also prove that our scheme is chosen message and ID secure in the random oracle model,assuming the hardness of factoring.     

Notes on automata theory based on quantum logic

The main results are as follows: (1) it deals with a number of basic operations (concatenation, Kleene closure, homomorphism, complement); (2) due to a condition imposed on the implication operator for discussing some basic issues in orthomodular lattice-valued automata, this condition is investigated in detail, and it is discovered that all the relatively reasonable five implication operators in quantum logic do not satisfy this condition, and that one of the five implications satisfies such a condition iff the truth-value lattice is indeed a Boolean algebra; (3) it deals further with orthomodular lattice-valued successor and source operators; (4) an example is provided, implying that some negative results obtained in the literature may still hold in some typical orthomodular lattice-valued automata.     

Cryptanalysis on AW digital signature scheme based on error-correcting codes

In 1993, Alabhadi and Wicker gave a modification to Xinmei Digital Signature Scheme based on error-correcting codes, which is usually denoted by AW Scheme. In this paper we show that the AW Scheme is actually not secure: anyone holding public keys of the signatory can obtain the equivalent private keys, and then forge digital signatures for arbitrary messages successfully. We also point out that one can hardly construct a digital signature scheme with high-level security due to the difficulty of decomposing large matrixes.     

An improved formalism for quantum computation based on geometric algebra—case study: Grover’s search algorithm

The Grover search algorithm is one of the two key algorithms in the field of quantum computing, and hence it is desirable to represent it in the simplest and most intuitive formalism possible. We show firstly, that Clifford’s geometric algebra, provides a significantly simpler representation than the conventional bra-ket notation, and secondly, that the basis defined by the states of maximum and minimum weight in the Grover search space, allows a simple visualization of the Grover search analogous to the precession of a spin- ${\frac{1}{2}}$ particle. Using this formalism we efficiently solve the exact search problem, as well as easily representing more general search situations. We do not claim the development of an improved algorithm, but show in a tutorial paper that geometric algebra provides extremely compact and elegant expressions with improved clarity for the Grover search algorithm. Being a key algorithm in quantum computing and one of the most studied, it forms an ideal basis for a tutorial on how to elucidate quantum operations in terms of geometric algebra—this is then of interest in extending the applicability of geometric algebra to more complicated problems in fields of quantum computing, quantum decision theory, and quantum information.     

The forecasting model based on wavelet ν-support vector machine

Aiming at the series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the L²(Rⁿ) space (quadratic continuous integral space). A new wavelet support vector machine (WN ν-SVM) is proposed based on wavelet theory and modified support vector machine. A particle swarm optimization (PSO) algorithm is designed to select the best parameters of WN ν-SVM model in the scope of constraint permission. The results of application in car sale series forecasting show that the forecasting approach based on the PSOWN ν-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than PSOW ν-SVM and other traditional methods.     

Temperature prediction control based on least squares support vector machines

A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controUed plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm.     

A blind video watermarking scheme based on ICA and shot segmentation

With the development of network and multimedia techniques, the digital video copyright protection issues have become more and more important as digital video can be easily pro- duced, replicated, accessed and distributed. Digital video watermark is the ma…     

Formulas and algorithms for quantum differentiation of quantum Bernstein bases and quantum Bézier curves based on quantum blossoming

Using the homogeneous version of the quantum blossom, we derive formulas and algorithms for the quantum derivatives of quantum Bernstein bases and quantum Bézier curves.     

The forecasting model based on fuzzy novel ν-support vector machine

This paper presents a new version of fuzzy support vector machine to forecast multi-dimension fuzzy sample. By combining the triangular fuzzy theory with the modified ν-support vector machine, the fuzzy novel ν-support vector machine (FNν-SVM) is proposed, whose constraint conditions are less than those of the standard Fν-SVM by one, is proved to satisfy the structure risk minimum rule under the condition of probability. Moreover, there is no parameter b in the regression function of the FNν-SVM. To seek the optimal parameters of the FNν-SVM, particle swarm optimization is also proposed to optimize the unknown parameters of the FNν-SVM. The results of the application in sale forecasts confirm the feasibility and the validity of the FNν-SVM model. Compared with the traditional model, the FNν-SVM method requires fewer samples and has better forecasting precision.     

Electromechanical equipment state forecasting based on genetic algorithm – support vector regression

Prediction of electromechanical equipments state nonlinear and non-stationary condition effectively is significant to forecast the lifetime of electromechanical equipments. In order to forecast electromechanical equipments state exactly, support vector regression optimized by genetic algorithm is proposed to forecast electromechanical equipments state. In the model, genetic algorithm is employed to choose the training parameters of support vector machine, and the SVR forecasting model of electromechanical equipments state with good forecasting ability is obtained. The proposed forecasting model is applied to the state forecasting for industrial smokes and gas turbine. The experimental results demonstrate that the proposed GA-SVR model provides better prediction capability. Therefore, the method is considered as a promising alternative method for forecasting electromechanical equipments state.     

An intelligent forecasting model based on robust wavelet ν-support vector machine

Aiming at the problem of small samples, season character, nonlinearity, randomicity and fuzziness in product demand series, the existing support vector kernel does not approach the random curve of the demands time series in the L²(Rⁿ) space (quadratic continuous integral space). The robust loss function is also proposed to solve the shortcoming of ε-insensitive loss function during handling hybrid noises. A novel robust wavelet support vector machine (RW ν-SVM) is proposed based on wavelet theory and the modified support vector machine. Particle swarm optimization algorithm is designed to select the optimal parameters of RW ν-SVM model in the scope of constraint permission. The results of application in car demand forecasts show that the forecasting approach based on the RW ν-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than RW ν-SVM and other traditional methods.     

The incremental learning algorithm with support vector machine based on hyperplane-distance

This paper proves the problem of losing incremental samples’ information of the present SVM incremental learning algorithm from both theoretic and experimental aspects, and proposes a new incremental learning algorithm with support vector machine based on hyperplane-distance. According to the geometric character of support vector, the algorithm uses Hyperplane-Distance to extract the samples, selects samples which are most likely to become support vector to form the vector set of edge, and conducts the support vector machine training on the vector set. This method reduces the number of training samples and effectively improves training speed of incremental learning. The results of experiment performed on Chinese webpage classification show that this algorithm can reduce the number of training samples effectively and accumulate historical information. The HD-SVM algorithm has higher training speed and better precision of classification.     

An improved image blind identification based on inconsistency in light source direction

In this study, an improved image blind identification algorithm based on inconsistency in light source direction was proposed. And a new method defined as “neighborhood method” was presented, which was used to calculate surface normal matrix of image in the blind identification algorithm. For an image, there is an error function between its actual light intensity and calculated light intensity, and for different light source models, there are different constraint functions of light. Light source direction which makes both error function and constraint function get the minimum is the one we want to seek. On this basis, according to the error function and the corresponding constraint function, search means and the Hestenes–Powell multiplier method were used in the improved algorithm to calculate the light source direction for local and infinite light source images, respectively. Further, the authenticity of image can be determined by the inconsistency in light source direction of different areas in the image. Experimental results showed that the light source direction of different areas in an image could be calculated accurately, and then the image tampering can be detected effectively by the improved algorithm. Moreover, the performance of the improved algorithm of the proposed blind identification is superior to that of the existing one in terms of detection rate and time complexity.     

Comment on “flexible protocol for quantum private query based on B92 protocol”

In a recent paper (Quantum Inf Process 13:805–813, 2014), a flexible quantum private query (QPQ) protocol based on B92 protocol is presented. Here we point out that the B92-based QPQ protocol is insecure in database security when the channel has loss, that is, the user (Alice) will know more records in Bob’s database compared with she has bought.     

Notes on two temporal structure–based methods for blind extraction of fetal electrocardiogram

Blind extraction of fetal electrocardiogram from the composite maternal electrocardiogram without noise and with additive Gaussian white noise (AGWN) is achieved by Barros and Cicochi (Neural Computat 13:1995–2003) and by Li and Zhang (Neurocomputing 71:1538–1542), respectively. Theoretical analysis shows that these two algorithms are essentially the same. In this paper, an intelligible method is proposed, which is based on the eigenvalue decomposition of the cross-correlation of whitened source signals at a given time tag. Simulation experiments show that the proposed method has better extraction performance. Moreover, a great deal of experiments with AGWN show that Li and Zhang’s method is slightly inferior to the Barros and Cicochi’s, which is opposite to the claim by Li and Zhang.     

Parameter selection of support vector regression based on hybrid optimization algorithm and its application

Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,     

Entangled quantum Otto heat engines based on two-spin systems with the Dzyaloshinski–Moriya interaction

We construct an entangled quantum Otto engine based on spin-1/2 systems undergoing Dzyaloshinski–Moriya (DM) interaction within a varying magnetic field. We investigate the influence of the DM interaction on basic thermodynamic quantities, including heat transfer, work done, and efficiency and find that the DM interaction importantly influences the engine’s thermodynamics. We obtain an expression for engine efficiency, finding it to yield the same efficiency for antiferromagnetic and ferromagnetic coupling. A new upper bound, nontrivially consistent with the second law of thermodynamics, is derived for engine efficiency in the case of non-zero DM interaction.