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ISSN 1000-1239 CN 11-1777/TP

Table of Content

01 May 2019, Volume 56 Issue 5
DDoS Attack Detection Model Based on Information Entropy and DNN in SDN
Zhang Long, Wang Jinsong
2019, 56(5):  909-918.  doi:10.7544/issn1000-1239.2019.20190017
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The software defined networking (SDN) decouples the data layer and the control layer of the network, but the controller is in danger of “single node invalidation ”. Attackers launch DDoS attacks to disable the controller and threaten the safety of networks. This paper presents a DDoS detection model based on entropy and deep neural network (DNN), which includes the initial detection module based on entropy-based detection method and the further detection module based on DNN. The initial detection module finds out the suspicious traffic in the network preliminarily by calculating the entropy of source and destination IP address, and then the suspected abnormal traffic with DNN-based DDoS detection module confirms the anomaly traffic. Experiments show that this model has higher recognition rate and accuracy rate than the traditional detection algorithm based on entropy or machine learning. At the same time, the model can shorten the detection time and improve the efficiency of resource utilization.
Delay Tolerant Access Control Method Based on Spatio-Temporal Distribution of Access Requests
Chen Li, Deng Kun,Jiang Tao,Yue Guangxue,Li Panpan,Yang Jun, Xu Xubao
2019, 56(5):  919-928.  doi:10.7544/issn1000-1239.2019.20190016
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In ocean observations, the infrastructure providing wireless communication is sparsely deployed and the wireless observation nodes move very fast. The limited or even scarce wireless network resources are difficult to meet the uploading requirements of large-scale data collection. It is extremely urgent to study and solve the effective upload access control method for massive observation nodes to compete for scarce communication resources. Based on the historical data of the observation access request, the neural network is used to perform time series analysis on them, and then gets their future revenues based on probability. To maximize total revenue, this paper studies the optimization method of uploading access scheduling problem that guarantees the delay tolerance of observation data based on time series analysis. Unfortunately, it is an NP-hard problem (see in theorem 1). Therefore, the approximation algorithm of enhanced access control (P-RSA) is proposed based on the dynamic programming idea. Firstly, the wireless access requests with spatio-temporal dynamic features is a quantified. Secondly, performance parameters are generalized to “revenue”. Finally, simulation experiments are performed that the total revenues of access requests are analysed under different AP’s load conditions until the deadline. P-RSA’s effectiveness is verified by detailed simulation experiments than the existing algorithms.
Intelligent and Efficient Method for Optimal Penetration Path Generation
Wang Shuo, Wang Jianhua, Tang Guangming, Pei Qingqi, Zhang Yuchen, Liu Xiaohu
2019, 56(5):  929-941.  doi:10.7544/issn1000-1239.2019.20190012
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Considering the insider and unknown attack, to further improve the efficiency, an intelligent-efficient method for generating the optimal penetration path is put forward. Firstly, we define the two-layer threat penetration graph(TLTPG), where the lower layer is called host threat penetration graph(HTPG) and the upper layer is called network threat penetration graph(NTPG). Then, based on knowledge graph, we build the host resource knowledge graph(HRKG), which is used to generate the HTPG intelligently and efficiently. Further, utilizating the HTPG, we design the NTPG generation algorithm based on penetration information exchange. Finaly, we describe the algorithm of optimal penetration path generation by using the TLTPG. Experimental results show that the proposed method can improve the efficiency of generating the optimal penetration path under the condition that the insider and unknown attack are considered.
Network Defense Decision-Making Method Based on Stochastic Game and Improved WoLF-PHC
Yang Junnan, Zhang Hongqi, Zhang Chuanfu
2019, 56(5):  942-954.  doi:10.7544/issn1000-1239.2019.20180877
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At present, the method of network attack and defense analysis based on stochastic game adopts the assumption of complete rationality, but in the actual network attack-defense confrontation, it is difficult for both sides of attack and defense to meet the high requirement of complete rationality, which reduces the accuracy and guiding value of the existing methods. Based on the reality of network attack-defense confrontation, the influence of bounded rationality on attack-defense stochastic game is analyzed. Under the constraints of bounded rationality, a stochastic game model is constructed. Aiming at the problem of network state explosion, a method of extracting network state and attack-defense action based on attack-defense graph is proposed, which the game state space is effectively reduced. On this basis, WoLF-PHC algorithm in reinforcement learning is introduced to carry out bounded rational stochastic game analysis and design a defensive decision-making algorithm with online learning ability. By learning, the algorithm can obtain the optimal defense strategy for the current attacker. The obtained strategy is superior to the Nash equilibrium strategy of the existing attack-defense stochastic game model under bounded rationality. By introducing eligibility trace to improve WoLF-PHC, the learning speed of defenders is further improved. The experimental results verify the effectiveness and advancement of the proposed method.
DiffSec: A Differentiated Intelligent Network Security Service Model
Deng Li, Wu Weinan, Zhu Zhengyi, Chen Ming
2019, 56(5):  955-966.  doi:10.7544/issn1000-1239.2019.20190019
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Network security for our modern information society is more and more important, and what followed by the cost of network security is increasing. It is a challenging task to reduce the cost of network security as much as possible on the premise of ensuring network security. Based on the fact that different user communities have different security requirements, this paper proposes a model called DiffSec that provides differentiated security services according to different user security levels. We argue that this model can effectively reduce the network security service cost and improve the network performance and can meet the needs of long-term development of the network security technology. Based on the DiffSec, we design the structure of the secure access network (SANet) and the corresponding intelligent control method using the combination of NFV and SDN, and implement the prototype system. The experimental results of the prototype system show that SANet can not only provide flexible and correct network security functions, but also has good network performance and practical value.
Implementation and Evaluation of Cooperative Routing in Software Defined Wireless Networking
Fei Ning, Xu Lijie, Cheng Xiaohui
2019, 56(5):  967-976.  doi:10.7544/issn1000-1239.2019.20180866
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In a mesh network, not all WiFi APs are always taken part in the actual packet transmission which makes network resources redundant. A same packet is usually received by more than one node even though they are not on this packet’s original transmission path. These nodes can be chosen to forward the packets to the destination node, which improves the bandwidth of the designated link in a cooperative routing path. However, due to the transmission and computation overhead on WiFi APs, the cooperative routing is difficult to be implemented in traditional wireless networking. The centralized architecture of software defined wireless networking (SDWN) makes it convenient to select helper nodes effectively and globally for a cooperative routing. This paper proposes a cooperative routing algorithm in SDWN. In this algorithm, routing discovery is performed on WiFi APs while the global and computation intensive tasks such as the route validation, the helper node selection and the interferences update are completed by the controller. By extending OpenFlow protocol, the proposed cooperative routing has been implemented and evaluated on a test bed. The evaluation indicates that our QoS oriented SDWN-based cooperative routing achieves greater bandwidth and less packet transmission delay than traditional wireless protocols.
Uncertain Data Clustering Algorithm Based on Voronoi Diagram in Obstacle Space
Wan Jing, Cui Meiyu, He Yunbin, Li Song
2019, 56(5):  977-991.  doi:10.7544/issn1000-1239.2019.20170979
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In order to solve the problem of the uncertain data clustering in obstacle space, the Voronoi diagram in computational geometry is introduced to divide the data space, and an uncertain data clustering algorithm based on Voronoi diagram in obstacle space is proposed. According to the properties of Voronoi diagram, four clustering rules are proposed. In order to consider the probability distribution between data, the KL distance is used as the similarity measure between data objects. Because obstacles can not always remain static in real life, and space obstacles often change dynamically. Then, according to whether the set of obstacles is changed, an uncertain data clustering algorithm in static obstacle environment and dynamic obstacle environment is proposed. Theoretical studies and experiments show that the uncertain refining clustering algorithm in the static obstacles environment(STAO_RVUBSCAN), the uncertain clustering algorithm of the dynamic increase of obstacles(DYNOC_VUBSCAN), the uncertain clustering algorithm of the dynamic reduction of obstacles(DYNOR_VUBSCAN) and the uncertain clustering algorithm of the dynamic movement of obstacles (DYNOM_VUBSCAN) have extremely high efficiency.
A Parallel Algorithm for Mining Interactive Features from Large Scale Sequences
Zhao Yuhai, Yin Ying, Li Yuan, Wang Siyao, Wang Guoren
2019, 56(5):  992-1006.  doi:10.7544/issn1000-1239.2019.20180276
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Sequence is an important type of data which is widely existing in various domains, and thus feature selection from sequence data is of practical significance in extensive applications. Interactive features refer to a set of features, each of which is weakly correlated with the target, but the whole of which is strongly correlated with the target. It is of great challenge to mine interactive features from large scale sequence data for the combinatorial explosion problem of loci. To address the problem, against the background of high-throughput sequencing in biology, a parallel evolutionary algorithm for high-order interactive features mining is proposed in this paper. Instead of sequence-block based parallel strategy, the work is inspired by loci-based idea since the number of loci is the fundamental factor that restricts the efficiency. Further, we propose the conception of maximal allelic common subsequence (MACS) and MACS based strategy for feature region partition. According to the strategy, the search range of interactive features is narrowed to many fragged spaces and interactions are guaranteed not to exist among different fragments. Finally, a parallel ant algorithm based on substitution search is developed to conduct interactive feature selection. Extensive experiments on real and synthetic datasets show that the efficiency and effectiveness of the proposed PACOIFS algorithm is superior to that of competitive algorithms.
Data Driven Prediction for the Difficulty of Mathematical Items
Tong Wei, Wang Fei, Liu Qi, Chen Enhong
2019, 56(5):  1007-1019.  doi:10.7544/issn1000-1239.2019.20180366
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The construction of item banking system is an important guarantee for the reform and development of educational examination, and meanwhile, is also an essential means to promote the modernization of examination. In such a system, item difficulty is one of the most important parameters, which has a direct influence on item designing, test paper organization, result report and even the fairness guarantee. Unfortunately, due to the unique education background and test characteristics in China, it is difficult to evaluate item difficulty through pre-test organization like some foreign countries. Thus, traditional efforts usually refer to the manual evaluation by expertise (e.g., experienced teachers). However, this way tends to be laborious, time-consuming and subjective in some way. Therefore, it is of great value to automatically judge the difficulty of items by information technology. Along this line, in this paper, we aim to propose a data-driven solution to predict the item difficulty in mathematics leveraged by the historical test logs and the corresponding item materials. Specifically, we propose a C-MIDP model and a R-MIDP model, which are based on CNN and RNN respectively, and further a hybrid H-MIDP model combined with both C-MIDP and R-MIDP. In the models, we directly learn item sematic representation from its text and train its difficulty with the statistic score rates among tests, where the whole modeling do not need any expertise, such as knowledge labeling. Then, we adopt a context-dependent training strategy considering the incomparability between different groups. Finally, with the trained models, we can predict each item difficulty only with its text input. Extensive experiments on a real-world dataset demonstrate that the proposed models perform very well.
The Framework of Protein Function Prediction Based on Boolean Matrix Decomposition
Liu Lin, Tang Lin, Tang Mingjing, Zhou Wei
2019, 56(5):  1020-1033.  doi:10.7544/issn1000-1239.2019.20180274
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Protein is the most essential and versatile macromolecule of living cells, and thus the research on protein functions is of great significance in decoding the secret of life. Previous researches have suggested that prediction of protein function is essentially a multi-label classification problem. Nonetheless, the large number of protein functional annotation labels brings the huge challenge to various kinds of multi-label classifiers applied to protein function prediction. To achieve more accuracy prediction of protein function by multi-label classifiers, we consider the characteristics of high correlation between protein functional labels, and propose a framework of protein function prediction based on Boolean matrix decomposition (PFP-BMD). Meanwhile, considering the problem of hardly satisfying exact decomposition and column in condition simultaneously of current Boolean matrix decomposition algorithms, an exact Boolean matrix decomposition algorithm based on label clusters is proposed, which realizes the hierarchical extended clustering of labels by the label-associated matrix. What’s more, we prove its ability of optimal Boolean matrix decomposition based on related deductions. The experimental results show that this exact Boolean matrix decomposition algorithm possesses considerable advantage in reducing the computational complexity in comparison with existing algorithms. In addition, the application of the proposed algorithm in PFP-BMD can effectively improve the accuracy of protein function prediction, and more importantly, reducing and restoring dimensions in the functional label space of proteins using this algorithm lays the foundation of a more efficient classification of various multi-label classifiers.
A Collaborative Filtering Recommendation Algorithm for Multi-Source Heterogeneous Data
Wu Bin, Lou Zhengzheng, Ye Yangdong
2019, 56(5):  1034-1047.  doi:10.7544/issn1000-1239.2019.20180461
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With the rapid development of electronic e-commerce sites, data characteristics and realistic demands have changed. The data, which has main characteristics of large-scale, multi-source and heterogeneous, is playing an important role. However, these unique characteristics of electronic e-commerce systems make most of existing collaborative filtering methods difficult to be adapted for product recommendation. The immediate problem to be solved is how to integrate multi-source heterogeneous data to achieve the maximum value of big data. In this paper, we first analyze the characteristics of various data among different information sources, and design different modeling solutions. Then, we propose a novel recommendation model for the task of rating prediction, which makes it possible to mitigate the sparsity problem via seamlessly integrating multi-relational data and visual contents. Finally, we devise a computationally efficient learning algorithm named MSRA (multi-source heterogeneous information based recommendation algorithm), to optimize the proposed model. To verify the effectiveness of our proposed model, we conduct extensive experiments on a wide spectrum of large-scale Amazon datasets. Experimental results demonstrate that 1)the designed algorithm consistently and significantly outperforms several state-of-the-art collaborative filtering algorithms, and 2)our algorithm is capable of alleviating the item cold-start problem and helping obtain more accurate results of various items.
Anomaly Detection Algorithm Based on FCM with Adaptive Artificial Fish-Swarm
Xi Liang, Wang Yong, Zhang Fengbin
2019, 56(5):  1048-1059.  doi:10.7544/issn1000-1239.2019.20180099
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Anomaly detection algorithm has played a key role in many areas, and the anomaly detection based on fuzzy C-means (FCM) is one of its representative methods. Owing to the limits of FCM such as the local minimum and the sensitiveness of the selection of initial value, there is still a large room to improve the conditional FCM-based anomaly detection method. In this paper, we firstly propose an adaptive artificial fish-swarm algorithm (AAFSA), by introducing an adaptive mechanism implemented by adjusting the value range of parameter “Visual” to the artificial fish-swarm algorithm which has a strong global search ability, to improve local and global optimization abilities and reduce the times of iterations. The limits of FCM mentioned above therefore can be solved by using the optimal solution obtained from AAFSA. Then, an anomaly detection algorithm based on AAFSA-FCM is designed by making full use of advantages of AAFSA to enhance the detection performances of anomaly detection algorithm. The experimental results show that the algorithm improves the detection performance both efficiently and effectively, which provides an effective solution for solving the problems of detection rate and false alarm rate in anomaly detection models, and state-of-the-art results achieve the purpose of reducing computational costs.
Noise Level Estimation Algorithm Using Convolutional Neural Network-Based Noise Separation Model
Xu Shaoping, Liu Tingyun, Li Chongxi, Tang Yiling, Hu Lingyan
2019, 56(5):  1060-1070.  doi:10.7544/issn1000-1239.2019.20180185
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The existing noise level estimation (NLE) algorithms usually adopt the strategy that separates the noise signal from the content of an image to estimate its noise level. Since only a single noisy image can be exploited, these algorithms usually design a variety of complex processes to ensure the accuracy of noise separation, resulting in low execution efficiency. To this end, a novel NLE algorithm using convolutional neural network (CNN)-based noise separation model is proposed in this paper. Specifically, we first add Gaussian noise with different levels to a great amount of representative undistorted images to obtain a training database. Then, we train a CNN-based noise separation model on the training database to obtain the noise mapping from a given noisy image. Considering the fact that the coefficients of the noise mapping show Gaussian distribution behavior, we utilize the generalized Gaussian distribution (GGD) to model the coefficients of the noise mapping, and use two parameters (scale and shape) of the model as the noise level-aware features (NLAF) to describe the level of a noisy image. Finally, an improved back propagation (BP) neural network is used to map the NLAF features to the final noise level. Extensive experiments demonstrate that our method outperforms the most existing classical NLE algorithms in terms of both computational efficiency and estimation accuracy, which makes it more practical to use.
Audio-Visual Correlated Multimodal Concept Detection
Dian Yujie, Jin Qin
2019, 56(5):  1071-1081.  doi:10.7544/issn1000-1239.2019.20180463
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With the wide dissemination of online video sharing applications, massive number of videos are generated online every day. Facing such massive videos, people require more refined retrieval services. How to organize and manage such massive videos on the Internet to help users retrieve videos more efficiently and accurately has become one of the most challenging topics in video analysis. In most scenarios, it is necessary that sounds and visual information appear simultaneously to decide a video event. Therefore, this paper proposes multimodal concept detection task based on audio-visual information. Firstly, a multimodal concept is defined as a noun-verb pair, in which the noun and verb represent visual and audio information separately. The audio and visual information in a multimodal concept is correlated. Secondly, this paper performs end-to-end multimodal concept detection using convolutional neural network. Specifically, audio-visual correlation is considered to train a joint learning network. The experimental results show that performance of the joint network via audio-visual correlation exceeds that of single visual or audio network. Thirdly, the joint network learns fine-grained features. In the Huawei video concept detection task, using visual features extracted from the joint network outperforms features extracted from an ImageNet pre-trained network on some specific concepts. In the ESC 50 audio classification task, acoustic features from the joint network exceeds that from VGG pre-trained on Youtube8m about 5.7%.
Denoising Autoencoder-Based Language Feature Compensation
Miao Xiaoxiao, Xu Ji, Wang Jian
2019, 56(5):  1082-1091.  doi:10.7544/issn1000-1239.2019.20180471
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Language identification (LID) accuracy is often significantly reduced when the duration of the test data and the training data are mismatched. This paper proposes a method to compensate language features using a denoising autoencoder (DAE). Use of denoising autoencoder-based language feature compensation can map language features from variable length utterances into a fixed length representation. Therefore the problem of length mismatch and unbalanced phoneme distribution can be mitigated. The algorithm first converts the speech signal to low level acoustic features by framing and transforming, and then estimates its i-vector and phonetic vector. These two vectors are then concatenated and fed into the DAE-based language feature compensation processing unit. The compensated i-vector from the output of the DAE, and the original i-vector, are presented to the back-end classifier to obtain two score vectors. These two score vectors are finally fused at a score level to obtain a final result. Tests on NIST-LRE07 demonstrate that this feature compensation method improves identification performance over various test speech durations. Compared with traditional LID systems, the performance for 30 s test utterances improves by 3.16%, while the performance for 10 s test utterances improves by 2.90%. Compared with the end-to-end LID system, the performance on 3 s test utterances is increased by 3.21%.
Static Restart Stochastic Gradient Descent Algorithm Based on Image Question Answering
Li Shengdong, Lü Xueqiang
2019, 56(5):  1092-1100.  doi:10.7544/issn1000-1239.2019.20180472
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Image question answering is a multimodal learning task intersecting computer vision and natural language processing. With the breakthroughs in the deep neural networks, it has been the hotspot and focus of many researchers’ attention. To solve the task, researchers put forward numerous excellent models. Stacked attention networks (SANs) is one of the most typical models, and gets the state-of-the-art results in the test of four public visual question answering datasets. Although it has the excellent performance, because of the diversity of question and the sparsity of answer, it cannot fully learn the universal law of the corpus, and easily fall into the poor local optimal solution, which leads to the higher question answering error rate. By analyzing the causes of the error and observing the details of the model processing image question answering, we find that stochastic gradient descent based on momentum (baseline) has some defects in the optimization of SANs. To solve it, we propose static restart stochastic gradient descent based on image question answering. The experimental results show that its accuracy is 0.29% higher than baseline, but its convergence rate is slower than baseline. To verify the significance of the improved performance, we conduct statistical hypothesis test on the experimental results. The results of T test prove that its improved performance is extremely significant in the process of converging to the global optimal solution. To verify its effectiveness in the same kind of algorithm, we conduct effectiveness experiments with it and the state-of-the-art first-order optimization algorithms. The experimental results and analysis prove that it is more effective in solving image question answering. To verify its generalization performance and promotion value, we conduct the image recognition experiment on the classic Cifar-10 for the image recognition task. The experimental results and the results of T test prove that it has good generalization performance and promotion value in the process of converging to the global optimal solution.
ElGamal Broadcasting Multi-Signcryption Protocol with UC Security
Li Jianmin, Yu Huifang, Xie Yong
2019, 56(5):  1101-1111.  doi:10.7544/issn1000-1239.2019.20180130
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Multi-signcryption means two or more parties sign the same message, moreover, the length of signcryption cannot linearly increase for the increasing of the number of signers. Although ordinary ElGamal multi-signature satisfies the unforgeability, however, it can’t resist joint attack of multiple signers. In order to overcome the shortcomings of existing ElGamal multi-signature, the authors integrate the techniques of ElGamal multi-signature and signcryption to present a new ElGamal broadcasting multi-signcryption (EBMSC) protocol. We also describe its algorithm definition and security model, and prove its semantical security under the discrete logarithm (DL) and computation Diffie-Hellman (CDH) assumptions in the random oracle model (ROM). At the same time, we define the ideal function and the real protocol of EBMSC protocol under the universally composalble (UC) security framework, and then prove that the real protocol can realize the ideal function of EBMSC protocol. It also proves that the real protocol is unforgeable under unforgeability against adaptive chosen message attacks. Finally, the efficiency comparison between EBMSC protocol and existing protocols is given. Analysis results show our protocol not only is more efficient than existing protocols but also implements the function of multi-signcryption in UC security framework. Our protocol can be suitable for applications in e-commerce, contract signing, online transaction and financial accounting.
Efficient and Verifiable Encryption Scheme in Lightweight Narrowband Internet of Things Applications
Qian Hanjia, Wang Yihuai, Peng Tao, Chen Cheng, Luo Xizhao
2019, 56(5):  1112-1122.  doi:10.7544/issn1000-1239.2019.20180217
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Narrowband Internet of things (NB-IoT) is an important branch of the Internet. It can provide application-level services and achieve information intellectualization, relying on the powerful resource processing capability offered by cloud computing. However, due to the storage of data in different places, cloud platform service providers are not completely trusted. User data is exposed in a not completely secured environment and this brings many security problems, such as external malicious attack and cloud server collusion. Aiming at these NB-IoT’s issues like its terminal nodes are vulnerable to attacks, lacking in resources, limit in power consumption, a property-based cloud storage fast access control scheme is proposed. Under the background of multiple attribute authorities, an efficient and verifiable lightweight cryptographic encryption schemes is the goal. So using the idea of online/offline encryption and combining outsourced decryption technology, an online/offline and outsourced multi-authority ciphertext-policy attribute-based encryption scheme (OO-MA-CP-ABE) which can be secured from chosen-plaintext attack (CPA) is constructed. It improves the efficiency of the encryption and decryption algorithm while minimizing user’s computational overhead, quite suitable for terminal equipment with weak computing power and limited resources, and can further ensure the correctness of outsourced computing by verifying the algorithm as well. It also gives the security analysis of the lightweight NB-IoT application system under cloud computing environment, in order to ensure the flexible and extensible access control strategy and the confidentiality and privacy protection of user data during the resource sharing process. Finally, the performance analysis of the OO-MA-CP-ABE scheme is given, and compared with the existing schemes in terms of functionality, computational overhead and storage overhead.
Efficient Verifiable Outsourcing of Solving Large-Scale Linear Equations with Low Storage Overhead
Feng Da, Zhou Fucai, Wang Qiang, Wu Qiyu
2019, 56(5):  1123-1131.  doi:10.7544/issn1000-1239.2019.20180191
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This paper studies the secure outsourcing problem of large-scale linear equations, and proposes a new secure outsourcing scheme of large-scale linear equations in the fully malicious model. First, we construct a pseudo-random invertible sparse matrix generation algorithm involving pseudo-random number generator and the property of strictly diagonally dominant matrix. Then we combine this algorithm with the process of encoding/decoding dense matrix with sparse matrix and give the new outsourcing scheme. The client in our scheme only needs 1 round interaction with the server and can detect the misbehavior of the server with an overwhelming probability (fully verifiable). In addition, compared with the previous schemes which require expensive storage overhead, our scheme reduces the overhead of storage to a constant level for the first time. We give the theoretical proof of the correctness, privacy and unforgeability of our scheme. Besides, the scheme can successfully handle the equations with no solution with enough privacy in our model. We compare the scheme with others and indicate the proposed scheme is superior to the existing ones in terms of efficiency, verifiability and storage overhead and finally provide the experimental evaluation that demonstrates the efficiency of our algorithms and the storage overhead the client needs.