Home / Journals / CMC / Vol.56, No.1, 2018
Table of Content
  • Open Access

    ARTICLE

    Efficient Secure Data Provenance Scheme in Multimedia Outsourcing and Sharing

    Zhen Yang1,2, Yongfeng Huang1,2,*, Xing Li1,2, Wenyu Wang3
    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 1-17, 2018, DOI: 10.3970/cmc.2018.03697
    Abstract To cope with privacy leakage caused by multimedia outsourcing and sharing, data provenance is used to analyze leaked multimedia and provide reactive accountability. Existing schemes of multimedia provenance are based on watermarking protocols. In an outsourcing scenario, existing schemes face two severe challenges: 1) when data leakage occurs, there exists a probability that data provenance results can be repudiated, in which case data provenance tracking fails; and 2) when outsourced data are shared, data encryption transfer causes key management burden outside the schemes, and privacy leakage threatens users. In this paper, we propose a novel data provenance scheme with an… More >

  • Open Access

    ARTICLE

    An Advanced Quantum-Resistant Signature Scheme for Cloud Based on Eisenstein Ring

    Faguo Wu1,2, Xiao Zhang1,2, Wang Yao1,2, Zhiming Zheng1,2, Lipeng Xiang3, Wanpeng Li4
    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 19-34, 2018, DOI: 10.3970/cmc.2018.02664
    Abstract Signature, widely used in cloud environment, describes the work as readily identifying its creator. The existing signature schemes in the literature mostly rely on the Hardness assumption which can be easily solved by quantum algorithm. In this paper, we proposed an advanced quantum-resistant signature scheme for Cloud based on Eisenstein Ring (ETRUS) which ensures our signature scheme proceed in a lattice with higher density. We proved that ETRUS highly improve the performance of traditional lattice signature schemes. Moreover, the Norm of polynomials decreases significantly in ETRUS which can effectively reduce the amount of polynomials convolution calculation. Furthermore, storage complexity of… More >

  • Open Access

    ARTICLE

    acSB: Anti-Collision Selective-Based Broadcast Protocol in CR-AdHocs

    Yueyue Li1,*, Zhong Huang1, Yugang Ma2, Guangjun Wen1
    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 35-46, 2018, DOI: 10.3970/cmc.2018.03712
    Abstract As a fundamental operation in ad hoc networks, broadcast could achieve efficient message propagations. Particularl y in the cognitive radio ad hoc network where unlicensed users have different sets of available channels, broadcasts are carried out on multiple channels. Accordingly, channel selection and collision avoidance are challenging issues to balance the efficiency against the reliability of broadcasting. In this paper, an anti-collision selective broadcast protocol, called acSB, is proposed. A channel selection algorithm based on limited neighbor information is considered to maximize success rates of transmissions once the sender and receiver have the same channel. Moreover, an anti-collision scheme is… More >

  • Open Access

    ARTICLE

    A Novel Quantum Stegonagraphy Based on Brown States

    Zhiguo Qu1,*, Tiancheng Zhu2, Jinwei Wang1, Xiaojun Wang3
    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 47-59, 2018, DOI: 10.3970/cmc.2018.02215
    Abstract In this paper, a novel quantum steganography protocol based on Brown entangled states is proposed. The new protocol adopts the CNOT operation to achieve the transmission of secret information by the best use of the characteristics of entangled states. Comparing with the previous quantum steganography algorithms, the new protocol focuses on its anti-noise capability for the phase-flip noise, which proved its good security resisting on quantum noise. Furthermore, the covert communication of secret information in the quantum secure direct communication channel would not affect the normal information transmission process due to the new protocol’s good imperceptibility. If the number of… More >

  • Open Access

    ARTICLE

    A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data

    Xiaonian Wu1,*, Chuyun Zhang3, Runlian Zhang2, Yujue Wang2, Jinhua Cui4
    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 61-72, 2018, DOI: 10.3970/cmc.2018.02449
    Abstract There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as… More >

  • Open Access

    ARTICLE

    A Distributed LRTCO Algorithm in Large-Scale DVE Multimedia Systems

    Hangjun Zhou1,2,*, Guang Sun1, Sha Fu1, Wangdong Jiang1, Tingting Xie3, Danqing Duan1
    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 73-89, 2018, DOI: 10.3970/cmc.2018.02411
    Abstract In the large-scale Distributed Virtual Environment (DVE) multimedia systems, one of key challenges is to distributedly preserve causal order delivery of messages in real time. Most of the existing causal order control approaches with real-time constraints use vector time as causal control information which is closely coupled with system scales. As the scale expands, each message is attached a large amount of control information that introduces too much network transmission overhead to maintain the real-time causal order delivery. In this article, a novel Lightweight Real-Time Causal Order (LRTCO) algorithm is proposed for large-scale DVE multimedia systems. LRTCO predicts and compares… More >

  • Open Access

    ARTICLE

    Weighted Sparse Image Classification Based on Low Rank Representation

    Qidi Wu1, Yibing Li1, Yun Lin1,*, Ruolin Zhou2
    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 91-105, 2018, DOI: 10.3970/cmc.2018.02771
    Abstract The conventional sparse representation-based image classification usually codes the samples independently, which will ignore the correlation information existed in the data. Hence, if we can explore the correlation information hidden in the data, the classification result will be improved significantly. To this end, in this paper, a novel weighted supervised spare coding method is proposed to address the image classification problem. The proposed method firstly explores the structural information sufficiently hidden in the data based on the low rank representation. And then, it introduced the extracted structural information to a novel weighted sparse representation model to code the samples in… More >

  • Open Access

    ARTICLE

    A New Encryption-then-Compression Scheme on Gray Images Using the Markov Random Field

    Chuntao Wang1,2, Yang Feng1, Tianzheng Li1, Hao Xie1, Goo-Rak Kwon3
    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 107-121, 2018, DOI: 10.3970/cmc.2018.02477
    Abstract Compressing encrypted images remains a challenge. As illustrated in our previous work on compression of encrypted binary images, it is preferable to exploit statistical characteristics at the receiver. Through this line, we characterize statistical correlations between adjacent bitplanes of a gray image with the Markov random field (MRF), represent it with a factor graph, and integrate the constructed MRF factor graph in that for binary image reconstruction, which gives rise to a joint factor graph for gray images reconstruction (JFGIR). By exploiting the JFGIR at the receiver to facilitate the reconstruction of the original bitplanes and deriving theoretically the sum-product… More >

  • Open Access

    ARTICLE

    Machine Learning Based Resource Allocation of Cloud Computing in Auction

    Jixian Zhang1, Ning Xie1, Xuejie Zhang1, Kun Yue1, Weidong Li2,*, Deepesh Kumar3
    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 123-135, 2018, DOI: 10.3970/cmc.2018.03728
    Abstract Resource allocation in auctions is a challenging problem for cloud computing. However, the resource allocation problem is NP-hard and cannot be solved in polynomial time. The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution; however, these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions. By learning a small-scale training set, the prediction model can… More >

  • Open Access

    ARTICLE

    Reliable Medical Recommendation Based on Privacy-Preserving Collaborative Filtering

    Mengwei Hou1, Rong Wei1,*, Tiangang Wang1, Yu Cheng2, Buyue Qian3
    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 137-149, 2018, DOI: 10.3970/cmc.2018.02438
    Abstract Collaborative filtering (CF) methods are widely adopted by existing medical recommendation systems, which can help clinicians perform their work by seeking and recommending appropriate medical advice. However, privacy issue arises in this process as sensitive patient private data are collected by the recommendation server. Recently proposed privacy-preserving collaborative filtering methods, using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Privacy Preserving Medical Recommendation (PPMR) algorithm, which can protect patients’ treatment information and demographic… More >

  • Open Access

    ARTICLE

    An Image Steganography Algorithm Based on Quantization Index Modulation Resisting Scaling Attacks and Statistical Detection

    Yue Zhang1, Dengpan Ye2, Junjun Gan1, Zhenyu Li3, Qingfeng Cheng1,*
    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 151-167, 2018, DOI: 10.3970/cmc.2018.02464
    Abstract In view of the fact that the current adaptive steganography algorithms are difficult to resist scaling attacks and that a method resisting scaling attack is only for the nearest neighbor interpolation method, this paper proposes an image steganography algorithm based on quantization index modulation resisting both scaling attacks and statistical detection. For the spatial image, this paper uses the watermarking algorithm based on quantization index modulation to extract the embedded domain. Then construct the embedding distortion function of the new embedded domain based on S-UNIWARD steganography, and use the minimum distortion coding to realize the embedding of the secret messages.… More >

  • Open Access

    ARTICLE

    Determination of the Normal Contact Stiffness and Integration Time Step for the Finite Element Modeling of Bristle-Surface Interaction

    Libardo V. Vanegas-Useche1, Magd M. Abdel-Wahab2,3,4,*, Graham A. Parker5
    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 169-184, 2018, DOI: 10.3970/cmc.2018.01827
    Abstract In finite element modeling of impact, it is necessary to define appropriate values of the normal contact stiffness, Kn, and the Integration Time Step (ITS). Because impacts are usually of very short duration, very small ITSs are required. Moreover, the selection of a suitable value of Kn is a critical issue, as the impact behavior depends dramatically on this parameter. In this work, a number of experimental tests and finite element analyses have been performed in order to obtain an appropriate value of Kn for the interaction between a bristle of a gutter brush for road sweeping and a concrete… More >

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