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An efficient traceable data sharing scheme in cloud computing for mobile devices

International Journal of High Performance computing and Networking 2018年第2期12卷 156-165页

作者： Wang, Zhiying Ye, Jun Wang, Jianfeng Xidian University Xian710071 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China

Owing to the convenience of mobile devices and the advantages of cloud computing, it is a fashion to efficiently share data by mobile devices in cloud computing. However, it might be insecure to upload the plaintext directly to the remote cloud server. In order to solve the problem, the data should be encrypted by a secure encryption scheme. Most existing data sharing schemes in cloud computing are either insecure against collusion attack or suffer from a large computational overhead. In this paper, an efficient data sharing scheme in cloud computing for mobile devices is proposed based on convergent encryption and polynomial reconstruction techniques. Moreover, predecryption is used to further reduce the computation overhead of mobile devices. Finally, we utilise traitor tracing technology to track the users who have leaked private keys for their own benefit. Experimental results indicate that the proposed scheme is extremely suitable for mobile devices. © 2018 Inderscience Enterprises Ltd.

关键词： Cryptography

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Image retrieval based on ResNet and ITQ

Image retrieval based on ResNet and ITQ

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2nd International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

作者： Guijun Wang Baohua Qiang Xianchun Zou Jinyun Lu Southwest University Guangxi Cooperative Innovation Center of cloud computing and Big Data University of Electronic Technology Chongqing Educational Evaluation Institute

In recent years, more and more hash learning methods have been applied to solve largescale vision problems. It has been shown that learning hash function by using supervised information can boost hashing quality. The state-of-the-art image retrieval hashing methods based on visual features lacks of learning ability, the image expression ability is weak and the efficiency of largescale image retrieval is low. In this paper, we propose a new supervised hashing framework based on deep Residual Networks and Iterative Quantization hashing. Firstly, we exploit the learning abilities of deep residual network to mine the inherent hidden relationship of image content, extract deep feature descriptors, and increase the visual expression of images Secondly, Iterative Quantization Hashing is applied to learn from the high-dimensional image feature and map into low-dimensional hamming space and achieve compact Hash codes. Finally, image retrieval is accomplished in lowdimensional hamming space. Experimental results of MNIST, CIFAR-10, CIFAR-100 and Caltech 256 show that the expression ability of visual feature is effectively improved and the image retrieval performance is substantially boosted compared with other related methods.

关键词： Learning to Hash Deep Residual Network Iterative Quantization

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Malicious Website Detection Based on URLs Static Features

Malicious Website Detection Based on URLs Static Features

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2018 International Conference on Modeling, Simulation and Optimization(MSO 2018)

作者： Chun-ming WU Min LI Li YE Xian-chun ZOU Bao-hua QIANG School of Computer and Information Science Southwest University Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology

Real-time and effectiveness are two basic requirements in URL-based malicious website detection. Based on the analysis of malicious URLs construction pattern, this paper puts forward a method to detect malicious website based on URLs static features. All features used in classification are extracted using statistic and there is no need to online access to obtain additional information about website, which reduces detection time greatly. At the same time, a feature reduction method is given in feature vector construction. Combined with reasonable selection of machine learning algorithm, our method can achieve both high accuracy and high efficiency. A series of comparative experiments proved the effectiveness of our method.

关键词： Malicious URL Static features Feature extraction Machine learning

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Real-time network traffic classification based on CDH pattern matching 14

Real-time network traffic classification based on CDH patter...

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14th International Conference on Computational Intelligence and Security, CIS 2018

作者： Li, Xunzhang Wang, Yong Ke, Wenlong Feng, Hao School of Computer Science and Information Security Guilin University of Electronic Technology Guilin Guangxi541004 China School of Information and Communication Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin Guangxi541004 China

ISBN: (纸本)9781728101699

In recent years, with the rapid development of the Internet, the data scale of application behavior and application traffic have exploded. How to classify the real-time traffic of network becomes a big challenge. How to balance the accuracy and real-time of traffic classification is a difficult problem in technology. Therefore, this paper proposes a pattern matching real-time traffic classification method named PM, which first uses jpcap to accept network traffic data in real time, and then uses pattern matching to perform real-time matching traffic characteristics to achieve traffic classification. Among them, the use of the distributed message system kafka and the parallel computing framework Spark significantly improve the execution efficiency of the program. The experimental results show that PM has good performance in terms of accuracy. © 2018 IEEE.

关键词： Pattern matching

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Secure searchable encryption: a survey

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Journal of Communications and Information Networks 2017年第4期1卷 52-65页

作者： Wang, Yunling Wang, Jianfeng Chen, Xiaofeng State Key Laboratory of Integrated Service Networks (ISN) Xidian University Xian China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin China

cloud computing facilitates convenient and on-demand network access to a centralized pool of resources. Currently, many users prefer to outsource data to the cloud in order to mitigate the burden of local storage. However, storing sensitive data on remote servers poses privacy challenges and is currently a source of concern. SE (Searchable Encryption) is a positive way to protect users sensitive data, while preserving search ability on the server side. SE allows the server to search encrypted data without leaking information in plaintext data. The two main branches of SE are SSE (Searchable Symmetric Encryption) and PEKS (Public key Encryption with Keyword Search). SSE allows only private key holders to produce ciphertexts and to create trapdoors for search, whereas PEKS enables a number of users who know the public key to produce ciphertexts but allows only the private key holder to create trapdoors. This article surveys the two main techniques of SE: SSE and PEKS. Different SE schemes are categorized and compared in terms of functionality, efficiency, and security. Moreover, we point out some valuable directions for future work on SE schemes.

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Proving Linearizability of Concurrent Queues

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Journal of Computers (Taiwan) 2024年第5期35卷 91-103页

作者： Peng, Jie Wen, Tangliu Jiang, Dongming Department of Information Engineering Gannan University of Science and Technology Ganzhou China Ganzhou Cloud Computing and Big Data Research Center Ganzhou China School of Big Data Jiangxi Science & Technology Normal University Nanchang China

Linearizability is a commonly accepted correctness criterion for concurrent data structures. Concurrent queues are among the most fundamental concurrent data structures. In this paper, we present necessary and sufficient conditions for proving linearizability of concurrent queues, which make use of linearization of dequeue operations. The verification conditions intuitively express the "FIFO" semantics of concurrent queues and can be verified just by reasoning about the happened-before order of operations. We have successfully applied the proof technique to prove several challenging concurrent queues. We believe that our proof technique can be extended to the concurrent data structures which have the ordering requirements when their elements are removed. © 2024 Codon Publications. All rights reserved.

关键词： data structures

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Improving the microenvironment of tracking photovoltaic systems promotes soil organic carbon accumulation by mediating plant carbon inputs and microbial necromass retention

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Journal of Environmental Management 2025年 388卷

作者： Siqi Zhang Jirui Gong Rui Wang Kai Zheng Weiyuan Zhang Xuede Dong Yuxia Hu Guisen Yang Chenyi Yan Shangpeng Zhang Ruijing Wang Tong Wang State Key Laboratory of Earth Surface Processes and Resource Ecology MOE Engineering Research Center of Desertification and Blown-sand Control Faculty of Geographical Science Beijing Normal University No. 19 Xinjiekouwai Street Haidian District Beijing 100875 China CGN New Energy Holdings Co. Ltd. Hanwei International Plaza Fengtai District Beijing 100071 China Inner Mongolia Ganze Desert Technology Co. Ltd. High-tech industrial park cloud computing big data enterprise headquarters Kangbashi District Ordos 017004 China

Photovoltaic systems greatly reduce greenhouse gas emissions. However, the microenvironmental changes of photovoltaic modules affect soil organic carbon (SOC) and improve carbon sequestration in terrestrial ecosystems to mitigate global climate change is unclear. We analyzed the effects of different photovoltaic systems on SOC and its fractions in semi-arid grassland, and revealed the SOC formation and stability mechanisms. Tracking photovoltaic systems increased carbon stock, especially proportion of mineral-associated organic carbon (MAOC) in SOC by more than 10 %, and depended on the responses of plant inputs and microbial necromass to microenvironmental changes. Suitable amounts of light and water in the systems improved litter biomass and quality, and promoted MAOC accumulation by increasing the exogenous carbon supply and microbial activity. Such systems also reduced soil carbon loss by reducing heterotrophic respiration caused by low levels of organic acids and recruiting effective microorganisms such as Schizothecium and Lactobacillus . Moreover, changes in litter and root exudates promoted microbial biomass and enzyme activity, mediating the retention of microbial necromass and SOC. The contribution of bacterial necromass carbon to SOC increased by more than 8.5 %. However, fixed photovoltaic systems reduced soil carbon stock by 0.46 kg m −2 due to water limitations that decreased both plant carbon inputs and microbial necromass. Our results revealed the importance of plant inputs and microbial necromass in regulating SOC in photovoltaic systems, and demonstrated that photovoltaic systems can achieve synergies between CO 2 emission reduction and soil carbon sequestration. This provides new insights for formulating carbon management policy and promoting sustainable eco-economic development.

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Research on virtual machine placement in the cloud based on improved simulated annealing algorithm

Research on virtual machine placement in the cloud based on ...

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Proceedings of the Biannual World Automation Congress

作者： Nan Su Aiwu Shi Changnian Chen E Chen Yuxuan Wang Cloud Computing & Big Data Research Center Wuhan Textile University Wuhan China School of Computer Science and Technology Wuhan University of Technology Wuhan China

How to efficiently place virtual machines is an important issue in the cloud data center. Virtual machine placement method based on traditional heuristic algorithm costs long time to reach an optimal allocation so that it is difficult to meet the real production environment. To solve this problem, this paper proposes a virtual machine placement method based on improved simulated annealing algorithm (ISA). Firstly, resource utilization model of servers and dynamic placement model for virtual machines are introduced. Secondly, two threshold values and an optimized annealing process are presented in the ISA. Finally, the ISA-based allocation weight vector is computed. Experimental results show that the method determines virtual machine placement in a short time span, achieves load balance and improves resource utilization rate in the cloud data center, as well as reduces the number of active servers.

关键词： Virtual machining Servers Resource management Simulated annealing cloud computing Heuristic algorithms Algorithm design and analysis

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Research on Technologies in data Fabric

Research on Technologies in Data Fabric

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Qingyuan Hu Zheng Yin Tao Tao Jibin Wang Zhuo Chen Bohuan Ai Yu Liu Chongzhou Liu China Mobile Information Technology Center Beijing China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

With the continuous advancement of technologies like big data, artificial intelligence, and cloud computing, enterprises are increasingly encountering challenges related to the heterogeneity of data from various sources, difficulties in querying data assets, and understanding business insights. To address these issues, data fabric emerges as a solution that leverages distributed data management. By harnessing metadata and facilitating its activation, data fabric enables organizations to efficiently and accurately manage and locate data assets. However, it is important to note that this concept is still under development and faces obstacles such as establishing connections with additional data sources and unifying semantics. Nonetheless, overall, the concept of data fabric exhibits remarkable potential in assisting enterprises in transitioning towards becoming data-driven organizations.

关键词： Industries cloud computing Technological innovation Soft sensors Standards organizations Distributed databases Collaboration

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data Catalogs with Artificial Intelligence and Active Metadata: A Case Study of China Mobile

Data Catalogs with Artificial Intelligence and Active Metada...

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Next Generation data-driven Networks (NGDN), International Conference on

作者： Jibin Wang Yanmei Guo Yong Jiang Jing Shang Zhuo Chen Yu Liu Qingyuan Hu Zheng Yin China Mobile Information Technology Center Beijing China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

In the context of the rapid development of artificial intelligence, telecommunications operators have accumulated an increasing amount of data. China Mobile has developed a set of practical methods of enhanced data catalog in response to current issues such as difficulties in scheduling heterogeneous data from multiple sources, lack of macroscopic views of data usage, unintelligent data operation methods, and high risk of data leakage. By leveraging data virtualization, active metadata management, machine learning, along with other large-scale model technologies, China Mobile has crafted an enhanced data catalog, improving the data management efficiency and business decision-making process, further strengthening its competitiveness in the telecommunication industry.

关键词： Industries Technological innovation Decision making Machine learning Metadata User experience Telecommunications Virtualization Next generation networking Business

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