In order to achieve secure signcryption schemes in the quantum era, Li Fagen et al. [Concurrency and Computation： Practice and Experience, 2012, 25（4）： 2112-2122] and Wang Fenghe et al. [Applied Mathematics ＆ Information Sciences, 2012, 6（1）： 23-28] have independently extended the concept of signcryption to lattice-based cryptography. However, their schemes are only secure under the random or- acle model. In this paper, we present a lattice-based signcryp- tion scheme which is secure under the standard model. We prove that our scheme achieves indistinguishability against adaptive chosen-ciphertext attacks （IND-CCA2） under the learning with errors （LWE） assumption and existential unforgeability against adaptive chosen-message attacks （EUF- CMA） under the small integer solution （SIS） assumption.
Xiuhua LUQiaoyan WENZhengping JINLicheng WANGChunli YANG
To resist the fast algebraic attack and fast selective discrete Fourier transform attacks,spectral immunity of a sequence or a Boolean function was proposed.At the same time,an algorithm to compute the spectral immunity of the binary sequence with odd period N was presented,here N is a factor of 2^n-1,where n is an integer.The case is more complicated when the period is even.In this paper,we compute linear complexity of every orthogonal sequence of a given sequence using Chan-Games algorithm and k-error linear complexity algorithm.Then,an algorithm for spectral immunity of binary sequence with period N=2^n is obtained.Furthermore,the time complexity of this algorithm is proved to be O（n）.
Existing research on image classification mainly used the artificial definition as the pre-training of the original image,which cost a lot of time on adjusting parameters.However,the depth of learning algorithm intends to make the computers automatically choose the most suitable features in the training process.The substantial of deep learning is to train mass data and obtain an accurate classification or prediction without any artificial work by constructing a multi-hidden-layer model.However,current deep learning model has problems of local minimums when choosing a constant learning rate to solve non-convex objective cost function in model training.This paper proposes an algorithm based on the Stacked Denoising Autoencoders(SDA)to solve this problem,and gives a contrast of different layer designs to test the performance.A MNIST database of handwritten digits is used to verify the effectiveness of this model..
Quantum pseudo-telepathy(QPT)is a new type of game where the quantum team can win with certainty while the classical one cannot.It means the advantages of quantum participants over classical ones in game.However,there has been no systematic and formal analysis on the QPT game before.Here we present the formal description of the QPT game and the definition of the most simplified QPT.Based on the above definitions,we simplify a famous QPT game,i.e.the Cabllo game.Then,according to some instances,we analyze the minimum best success probability by classical strategies of the two-player QPT,which reflects the advantage of the quantum strategies.Finally,we prove the best success probability by classical strategies for the most simplified QPT is totally related to the number of all possible question combinations.
