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Privacy Preservation of Users in P2P E-Payment System 3

Privacy Preservation of Users in P2P E-Payment System

3rd IEEE International Conference on Electrical, computer and Communication Technologies: computer science and engineering, ICECCT 2019

作者： Kwansah Ansah, Albert Kofi Adu-Gyamfi, Daniel Anokye, Stephen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China University of Mines and Technology Ghana School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Sichuan610054 China Universityy of Energy and Natural Resources Ghana

ISBN: (纸本)9781538681572

Bitcoin ecosystem is supposed to be anonymous and untraceable. Nonetheless, Bitcoin offers weak anonymity in practice. The linkable pseudonymity of Bitcoin system raises privacy concerns for users. There are inputs and outputs of cryptocurrencies that link to Bitcoin public addresses. This is vulnerable to possible linkability and traceability of users' identity, that can lead to information leakage. Input and output addresses of transactions miss unlinkability in Bitcoin. Several attempts to solve unlinkability and untraceability of users' transactions found not to satisfy all requirements of a practical anonymity for users to transact business with bitcoins securely and privately. In this paper, the authors focused on preserving identity and transactional behaviour of users in bitcoin cryptocurrency transactions. A secure privacy-preserving scheme is presented. The paper incorporates bilinear pairing, elliptic curve (ECC), ring signature and Zero Knowledge Proof to curb users' privacy breaches. The authors theoretically analysed, and evaluated proposed scheme, which is proven secure and robust to implement for preserving users' privacy in bitcoin transactions. The proposal adds a compatible privacy-preserving layer on top of the Bitcoin blockchain, and consistent with the current Bitcoin architecture without offering any modification. © 2019 IEEE.

关键词： Blockchain

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Fast Network Alignment via Graph Meta-Learning

Fast Network Alignment via Graph Meta-Learning

引用

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Fan Zhou Chengtai Cao Goce Trajcevski Kunpeng Zhang Ting Zhong Ji Geng School of Information and Software Engineering University of Electronic Science and Technology of China Department of Electrical and Computer Engineering Iowa State University Ames IA Robert H. Smith School of Business University of Maryland College park MD

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Network alignment (NA) - i.e., linking entities from different networks (also known as identity linkage) - is a fundamental problem in many application domains. Recent advances in deep graph learning have inspired various auspicious approaches for tackling the NA problem. However, most of the existing works suffer from efficiency and generalization, due to complexities and redundant *** approach the NA from a different perspective, tackling it via meta-learning in a semi-supervised manner, and propose an effective and efficient approach called Meta-NA - a novel, conceptually simple, flexible, and general framework. Specifically, we reformulate NA as a one-shot classification problem and address it with a graph meta-learning framework. Meta-NA exploits the meta-metric learning from known anchor nodes to obtain latent priors for linking unknown anchor nodes. It contains multiple sub-networks corresponding to multiple graphs, learning a unified metric space, where one can easily link entities across different graphs. In addition to the performance lift, Meta-NA greatly improves the anchor linking generalization, significantly reduces the computational overheads, and is easily extendable to multi-network alignment scenarios. Extensive experiments conducted on three real-world datasets demonstrate the superiority of Meta-NA over several state-of-the-art baselines in terms of both alignment accuracy and learning efficiency.

关键词： Task analysis Feature extraction Measurement Data privacy Testing Training

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Plot2API: Recommending graphic API from plot via semantic parsing guided neural network

arXiv

引用

arXiv 2021年

作者： Wang, Zeyu Huang, Sheng Liu, Zhongxin Yan, Meng Xia, Xin Wang, Bei Yang, Dan Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Faculty of Information Technology Monash University Australia Pengcheng Laboratory Shenzhen China

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely imbalanced. Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API categories. We collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs. © 2021, CC BY-NC-SA.

关键词： Data visualization

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Adversarial Learning with Cost-Sensitive Classes

arXiv

引用

arXiv 2021年

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

It is necessary to improve the performance of some special classes or to particularly protect them from attacks in adversarial learning. This paper proposes a framework combining cost-sensitive classification and adversarial learning together to train a model that can distinguish between protected and unprotected classes, such that the protected classes are less vulnerable to adversarial examples. We find in this framework an interesting phenomenon during the training of deep neural networks, called Min-Max property, that is, the absolute values of most parameters in the convolutional layer approach zero while the absolute values of a few parameters are significantly larger becoming bigger. Based on this Min-Max property which is formulated and analyzed in a view of random distribution, we further build a new defense model against adversarial examples for adversarial robustness improvement. An advantage of the built model is that it performs better than the standard one and can combine with adversarial training to achieve an improved performance. It is experimentally confirmed that, regarding the average accuracy of all classes, our model is almost as same as the existing models when an attack does not occur and is better than the existing models when an attack occurs. Specifically, regarding the accuracy of protected classes, the proposed model is much better than the existing models when an attack occurs. © 2021, CC BY.

关键词： Deep neural networks

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A Fuzzy Neural Network Based Dynamic Data Allocation Model on Heterogeneous Multi-GPUs for Large-scale Computations

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International Journal of Automation and computing 2018年第2期15卷 181-193页

作者： Chao-Long Zhang Yuan-Ping Xu Zhi-Jie Xu Jia He Jing Wang Jian-Hua Adu School of Software Engineering Chengdu University of Information Technology School of Computing & Engineering University of Huddersfield School of Computer Science Chengdu University of Information Technology Department of Computing Sheffield Hallam University

The parallel computation capabilities of modern graphics processing units （GPUs） have attracted increasing attention from researchers and engineers who have been conducting high computational throughput studies. However, current single GPU based engineering solutions are often struggling to fulfill their real-time requirements. Thus, the multi-GPU-based approach has become a popular and cost-effective choice for tackling the demands. In those cases, the computational load balancing over multiple GPU ＂nodes＂ is often the key and bottleneck that affect the quality and performance of the real=time system. The existing load balancing approaches are mainly based on the assumption that all GPU nodes in the same computer framework are of equal computational performance, which is often not the case due to cluster design and other legacy issues. This paper presents a novel dynamic load balancing （DLB） model for rapid data division and allocation on heterogeneous GPU nodes based on an innovative fuzzy neural network （FNN）. In this research, a 5-state parameter feedback mechanism defining the overall cluster and node performance is proposed. The corresponding FNN-based DLB model will be capable of monitoring and predicting individual node performance under different workload scenarios. A real=time adaptive scheduler has been devised to reorganize the data inputs to each node when necessary to maintain their runtime computational performance. The devised model has been implemented on two dimensional （2D） discrete wavelet transform （DWT） applications for evaluation. Experiment results show that this DLB model enables a high computational throughput while ensuring real=time and precision requirements from complex computational tasks.

关键词： Heterogeneous GPU cluster dynamic load balancing fuzzy neural network adaptive scheduler discrete wavelet trans-form.

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A Multivariate Time Series Prediction Schema based on Multi-attention in recurrent neural network

A Multivariate Time Series Prediction Schema based on Multi-...

引用

IEEE Symposium on computers and Communications (ISCC)

作者： Xiang Yin Yanni Han Hongyu Sun Zhen Xu Haibo Yu Xiaoyu Duan Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9781728180861

ISBN: (纸本)9781728180878

In the past decades, various approaches have been proposed to address the time series prediction problem, among which nonlinear autoregressive exogenous (NARX) models achieve great progresses in one-step time prediction. Although NARX models are capable of capturing long-term dependence of the time series data, the impact of associated attributes lacks enough attention. To cope with this issue, in this paper we propose a Multi-Attention algorithm based Recurrent Neural Network (RNN) to perform multivariate time series forecasting. In the first stage, given a raw multivariate time series segment, we obtain both relevant encoder hidden state and encoder hidden state of the associated attribute by employing input-attention and self-attention respectively. In the second stage, we use temporal-convolution-attention neural network to process the encoder hidden states and capture long-range temporal patterns. Finally, extensive empirical studies tested with four real world datasets (NASDAQ100, SML2010, Gas Sensor Array Temperature Modulation and Air Quality) demonstrate the effectiveness and robustness of our proposed approach.

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Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds

arXiv

引用

arXiv 2020年

作者： Xu, Zichuan Zhou, Pan Wu, Jiangkai Ren, Jiankang Xia, Qiufen Liang, Huizhi Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Software Dalian University of Technology Dalian China School of Computer Science Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network Service Software of Liaoning Province International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science University of Reading Reading United Kingdom

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers

arXiv

引用

arXiv 2021年

作者： Materwala, Huned Ismail, Leila Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in a cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to tradeoff the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021, CC BY.

关键词： Energy efficiency

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[Retracted] Cyber Security against Intrusion Detection Using Ensemble-Based Approaches

引用

Security and Communication Networks 2023年第1期2023卷

作者： Mohammed Naif Alatawi Najah Alsubaie Habib Ullah Khan Tariq Sadad Hathal Salamah Alwageed Shaukat Ali Islam Zada Information Technology Department Faculty of Computing and Information Technology University of Tabuk Tabuk Saudi Arabiaut.edu.sa Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabiapnu.edu.sa Department of Accounting and Information Systems College of Business and Economics Qatar University Doha Qatarqu.edu.qa Department of Computer Science University Engineering and Technology Mardan Khyber Pakhtunkhwa Pakistan College of Computer and Information Sciences Jouf University Sakakah Saudi Arabiaju.edu.sa Department of Computer Science University of Peshawar Peshawar Khyber Pakhtunkhwa Pakistanuop.edu.pk Department of Software Engineering Faculty of Computing & Information Technology International Islamic University Islamabad Pakistaniiu.edu.pk

The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications.

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Toward accurate link between code and software documentation

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science China(information sciences) 2018年第5期61卷 72-86页

作者： Yingkui CAO Yanzhen ZOU Yuxiang LUO Bing XIE Junfeng ZHAO Key Laboratory of High Confidence Software Technologies Ministry of Education School of Electronics Engineering and Computer Science Peking University Beida (Binhai) Information Research

Recovering traceability links between source code and software documentation is an important research topic in software maintenance and software reuse. There have been a lot of research efforts in recovering traceability between documentation and code elements(class, interface, method, etc.), mostly based on program analysis. However, there are still a lot of noise links being established in existing work. In this paper, we propose a novel approach to classifying code elements, occurring in a document, into contextual code elements and salient code elements. As a result, we can filter the noise traceability links between a software document and its contextual code elements and get a higher quality link set. Our classifier is trained based on open source project Lucene's source code and 1899 Stack Overflow answer documents about *** extract code elements from these documents and represent each of these code elements with a 7-dimension feature vector, then we use a decision-tree-based learning model to classify them as salient or not. In the experiments, we get a precision of 70.7% in recognizing the salient code elements of these documents and get12% improvement compared with Rigby's work. We can filter out 56.5%～69.3% noise traceability links with different thresholds in our classifier. It can improve the quality of traceability links between source code and their related software documents in application.

关键词： traceability link code element software document contextual code element salient codeelement

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