Yixian Yang

Bio: Yixian Yang is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Encryption & Synchronization (computer science). The author has an hindex of 42, co-authored 444 publications receiving 6568 citations. Previous affiliations of Yixian Yang include Guizhou University & Huawei.

An efficient protocol for the private comparison of equal information based on the triplet entangled state and single-particle measurement

Abstract: The central theme of this paper is that we propose an efficient protocol for comparing the equal information with the help of a third party (TP). We assume that TP is semi-honest, i.e., TP executes the protocol loyally, keeps a record of all its intermediate computations and might try to steal the players’ private inputs from the record, but he cannot be corrupted by the adversary. The security of this protocol with respect to various kinds of attacks is discussed. Our protocol utilizes the triplet entangled states and the simple single-particle measurement. The particles carried the secret messages do not be repeatedly transmitted. The players’ messages are divided into many groups. Sometimes, the protocol is already successfully completed, but all data are not compared. Thus, many time and huge quantum resources can be saved.

Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network

Abstract: Intrusion detection systems play an important role in preventing security threats and protecting networks from attacks. However, with the emergence of unknown attacks and imbalanced samples, traditional machine learning methods suffer from lower detection rates and higher false positive rates. We propose a novel intrusion detection model that combines an improved conditional variational AutoEncoder (ICVAE) with a deep neural network (DNN), namely ICVAE-DNN. ICVAE is used to learn and explore potential sparse representations between network data features and classes. The trained ICVAE decoder generates new attack samples according to the specified intrusion categories to balance the training data and increase the diversity of training samples, thereby improving the detection rate of the imbalanced attacks. The trained ICVAE encoder is not only used to automatically reduce data dimension, but also to initialize the weight of DNN hidden layers, so that DNN can easily achieve global optimization through back propagation and fine tuning. The NSL-KDD and UNSW-NB15 datasets are used to evaluate the performance of the ICVAE-DNN. The ICVAE-DNN is superior to the three well-known oversampling methods in data augmentation. Moreover, the ICVAE-DNN outperforms six well-known models in detection performance, and is more effective in detecting minority attacks and unknown attacks. In addition, the ICVAE-DNN also shows better overall accuracy, detection rate and false positive rate than the nine state-of-the-art intrusion detection methods.

Cryptographic primitives in blockchains

Abstract: Blockchain, as one of the crypto-intensive creatures, has become a very hot topic recently. Although many surveys have recently been dedicated to the security and privacy issues of blockchains, there still lacks a systematic examination on the cryptographic primitives in blockchains. To this end, we in this paper conduct a systematic study on the cryptographic primitives in blockchains by comprehensive analysis on top-30 mainstream cryptocurrencies, in terms of the usages, functionalities, and evolutions of these primitives. We hope that it would be helpful for cryptographers who are going to devote themselves to the blockchain research, and the financial engineers/managers who want to evaluate cryptographic solutions for blockchain-based projects.

An Enhanced Biometric-Based Authentication Scheme for Telecare Medicine Information Systems Using Elliptic Curve Cryptosystem

Abstract: The telecare medical information systems (TMISs) enable patients to conveniently enjoy telecare services at home. The protection of patient's privacy is a key issue due to the openness of communication environment. Authentication as a typical approach is adopted to guarantee confidential and authorized interaction between the patient and remote server. In order to achieve the goals, numerous remote authentication schemes based on cryptography have been presented. Recently, Arshad et al.(J Med Syst 38(12): 2014) presented a secure and efficient three-factor authenticated key exchange scheme to remedy the weaknesses of Tan et al.'s scheme (J Med Syst 38(3): 2014). In this paper, we found that once a successful off-line password attack that results in an adversary could impersonate any user of the system in Arshad et al.'s scheme. In order to thwart these security attacks, an enhanced biometric and smart card based remote authentication scheme for TMISs is proposed. In addition, the BAN logic is applied to demonstrate the completeness of the enhanced scheme. Security and performance analyses show that our enhanced scheme satisfies more security properties and less computational cost compared with previously proposed schemes.

Joint remote preparation of an arbitrary three-qubit state

Abstract: In this paper, by constructing some useful measurement bases, we first show that two senders can jointly prepare a three-qubit state of complex coefficients to a remote receiver via the shared three GHZ states. Then, the success probability can be improved by using the permutation group to classify the preparation state. Furthermore, under some different measurement bases, we propose another scheme to jointly prepare a three-qubit state of real coefficients with less restrictions. Finally, the present schemes are extended to multi-sender, and the classical communication costs of all the schemes are also calculated.

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

The structure and dynamics of multilayer networks

Abstract: In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporal- or context-related properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of real-world systems, and explicitly considered the time-varying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.

Image Processing

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.