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arXiv 2019年

作者： Zhao, Fangyuan Ren, Xuebin Yang, Shusen Yang, Xinyu School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University China

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the collapsed gibbs sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

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RETRACTED ARTICLE: Cloud enabled robust authenticated key agreement scheme for telecare medical information system

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Connection Science 2021年第4期33卷 I页

作者： Liu, Tian Liu, Xuchong Li, Xiong Amin, Ruhul Liang, Wei Meng-Yen, Hsieh School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan People's Republic of China Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police Academy Changsha People's Republic of China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu People's Republic of China Department of Computer Science and Engineering Dr. SPM International Institute of Information Technology Naya Raipur India College of Computer Science and Electronic Engineering Hunan University Changsha People's Republic of China Deptment of Computer Science and Information Engineering Providence University Taichung Taiwan

We, the authors, Editors and Publisher of the journal Connection Science, have retracted the following article:Tian Liu, Xuchong Liu, Xiong Li, Ruhul Amin, Wei Liang & Meng-Yen Hsieh (2021) Cloud enabled robust authenticated key agreement scheme for telecare medical information system, Connection Science, DOI: 10.1080/09540091.2021.1901072Since publication, the authors identified some technical issues that they had overlooked. In general, biometrics are data that owns fuzzy properties but the authors in this paper have neglected this technical point and the biometric information was authenticated using a hash function. Since the output of a hash function is sensitive to the input it cannot be applied to verify the validity of fuzzy data such as biometrics. Since this scheme is based on a hash function for biometric verification, it must not be used in real applications. The authors alerted the issue to the Editor and Publisher and all have agreed to retract the article to ensure the integrity of the scholarly *** have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on *** retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.

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Team: An taylor expansion-based method for generating adversarial examples

arXiv

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arXiv 2020年

作者： Qian, Ya-guan Zhang, Xi-Ming Swaileh, Wassim Wei, Li Wang, Bin Chen, Jian-Hai Zhou, Wu-Jie Lei, Jing-Sheng School of Big-Data Science Zhejiang University of Science and Technology Hangzhou310023 China Network and Information Security Laboratory of Hangzhou Hikvision Digital Technology Co. Ltd. Hang Zhou310052 China College of Computer Science and Technology Zhejiang University Hangzhou310058 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Laboratoire ETIS UMR-CNRS 8051 CY Cergy Paris Université France

Although Deep Neural networks(DNNs) have achieved successful applications in many fields, they are vulnerable to adversarial *** training is one of the most effective methods to improve the robustness of DNNs, and it is generally considered as solving a saddle point problem that minimizes risk and maximizes ***, powerful adversarial examples can effectively replicate the situation of perturbation maximization to solve the saddle point *** method proposed in this paper approximates the output of DNNs in the input neighborhood by using the Taylor expansion, and then optimizes it by using the Lagrange multiplier method to generate adversarial examples. If it is used for adversarial training, the DNNs can be effectively regularized and the defects of the model can be improved. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A chaotic artificial bee colony algorithm based on lévy search

A chaotic artificial bee colony algorithm based on lévy sea...

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作者： Lin, Shijie Dong, Chen Wang, Zhiqiang Guo, Wenzhong Chen, Zhenyi Ye, Yin College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Key Laboratory of Information Security of Network Systems Fuzhou350116 China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China Department of Electrical Engineering University of South Florida TampaFL33620 United States

A Lévy search strategy based chaotic artificial bee colony algorithm (LABC) is proposed in this paper. The chaotic sequence, global optimal mechanism and Lévy flight mechanism were introduced respectively into the initialization, the employed bee search and the onlooker bee search. The experiments show that the proposed algorithm performed better in convergence speed, global search ability and optimization accuracy than other improved ABC. Copyright © 2018 The Institute of Electronics, Information and Communication Engineers.

关键词： Evolutionary algorithms

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Slice-Based Window Detection from Scene Point Clouds

Slice-Based Window Detection from Scene Point Clouds

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International Conference on Virtual Reality and Visualization (ICVRV)

作者： Wen Hao Yinghui Wang Wei Liang Xiaojuan Ning Ye Li Institute of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9781538684986;9781538684979

In this paper, we propose a comprehensive strategy to detect the windows from scene point clouds. First, the planar points are extracted after computing the dimensionality structure of each point. The planar points are then grouped into clusters based on the normal direction and distance between points. Next, the building walls are extracted from the scene point clouds according to a collection of characteristics such as surface area, normal direction, and topological relationship. Finally, the building facade is sliced both horizontally and vertically. A slice-based algorithm based on the point number is proposed to locate the windows. The experimental results demonstrate that our method can detect the window location quickly and effectively.

关键词： Windows Three-dimensional displays Microsoft Windows Two dimensional displays Clustering algorithms Feature extraction

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A Provably Secure General Construction for Key Exchange Protocols Using Smart Card and Password

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Chinese Journal of Electronics 2017年第2期26卷 271-278页

作者： ZHANG Gefei FAN Dan ZHANG Yuqing LI Xiaowei National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences

Key exchange protocols using both smart card and password are widely used nowadays since they provide greater convenience and stronger security than protocols using only a *** of these protocols are often limited to simple network systems,and they may have security *** propose a general construction for key exchange protocols using smart card and password to avoid these *** constructed protocol from the general construction has only one additional communication round than the original public encryption *** construction is proven secure under random oracle model,so it can resist several common types of *** is also adapted well to various *** with related protocols,the proposed key exchange protocol generated from the general construction has better secure properties and good computational efficiency in storage cost and operation time.

关键词： General construction Provable secure Smart card Password Key exchange protocol

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Depthwise Separable Convolution Feature Learning for Ihomogeneous Rock Image Classification 1

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4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018

作者： Zhu, Yueqin Bai, Lin Peng, Weihang Zhang, Xinyang Luo, Xiong Development and Research Center China Geological Survey Beijing100037 China Key Laboratory of Geological Information Technology Ministry of Natural Resources Beijing100037 China Geomathematics Key Laboratory of Sichuan Province Chengdu University of Technology Chengdu610059 China College of Geophysics Chengdu University of Technology Chengdu610059 China College of Management Science Chengdu University of Technology Chengdu610059 China College of Network Security Chengdu University of Technology Chengdu610059 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (数字)9789811379833

ISBN: (纸本)9789811379826

The issue of classifying rock images is one of most important problems in geological detection and analysis. In order to improve rock recognition performance, we use convolution neural network (CNN) based deep learning method to classify ihomogeneous rock images intelligently. We present the depthwise separable convolution method for rock image classification, which reduces the required parameters compared to normal convolution, and achieves the separation of channels and regions. In our experiments, there are 12 kinds of common rock image data collected by us. Generally, similar rocks such as limestone and dolomite are easy to be confused, which is the same as that of rock with naked eyes. Compared with the model Inception, the rock image classification accuracy can be improved 9% by using the depth separable convolution model Xception. Meanwhile, after analyzing feature maps of granite and slate generated by the proposed model, we can easily find that the color, mineral composition and structure of the rock image are extracted successfully. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Incentive Mechanism for Data Uploading in Mobile Crowdsensing

Incentive Mechanism for Data Uploading in Mobile Crowdsensin...

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International Conference on networking and network Applications (NaNA)

作者： Xiaolan Liu Wu Wang Bin Yang Yuanyuan Fan School of Computer and Information Engineering Chuzhou University Chuzhou P. R. China Shaanxi Key Laboratory of Network and System Security Xidian University Xi'an P. R. China School of Mathematics and Computer Science Yunnan Minzu University Kunming P. R. China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

Mobile crowdsensing is a promising service paradigm utilizing the pervasive mobile devices to cooperatively perform large-scale sensing tasks. Data uploading which aims to deliver the sensed data to the control center, is an important problem in mobile crowdsensing. Existing works assume that the devices participate in device-to-device (D2D) cooperation voluntarily to assist other devices for data uploading. In reality, to assist other devices to upload data, the devices are requirement of consuming their own resources (e.g., memory, channel and time), which affects their willingness to participate in D2D cooperation. To this limitation, we propose an incentive mechanism to stimulate efficient D2D cooperation among devices. With this incentive mechanism, the reward of each device is determined by the reduced data uploading latency. According to the incentive mechanism, we design a chain formation algorithm, where the nearby devices can cooperate to construct a D2D chain by employing bottom to top mode. Finally, we corroborate the effectiveness of our incentive mechanism in reducing the average data uploading latency by extensive simulations.

关键词： Device-to-device communication Sensors Performance evaluation Task analysis Fading channels Data models Indexes

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Exploiting Meta-Path with Attention Mechanism for Fine-Grained User Location Prediction in LBSNs

Exploiting Meta-Path with Attention Mechanism for Fine-Grain...

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2019 3rd International Conference on Data Mining, Communications and Information Technology (DMCIT 2019)

作者： Zhixiao Wang Wenyao Yan Wendong Wang Ang Gao Lei Yu Shaobo Yang GaoYang Nie School of Computer Science and Engineering Xi'an University of Technology Telematics Group the University of Goettingen Shaanxi Key Laboratory of Network Computing and Security School of Electronic and Information Engineering Xi'an Jiaotong University College of Xi'an Innovation Yan'an University Yan'an University National Satellite Meteorological Center China Meteorological Administration

Based on the huge volumes of user check-in data in LBSNs,users’ intrinsic mobility patterns can be well explored,which is fundamental for predicting where a user will visit next given his/her historical check-in *** there are various types of nodes and interactions in LBSNs,they can be treated as Heterogeneous Information network（HIN） where multiple semantic meta-paths can be *** by the recent success of meta-path context based embedding techniques in HIN,in this paper,we design a deep neural network framework leveraging various meta-path contexts for fine-grained user location *** results based on two real-world LBSN datasets demonstrate the best effectiveness of the proposed approach using various evaluation metrics than others.

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Multi-keyword Cipher-text Retrieval Method for Smart Grid Edge Computing

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Journal of Physics: Conference Series 2021年第1期1754卷

作者： Jing Zhu Tao Wu Jintao Li Yanbin Liu Qixin Jiang School of Communication and Information Engineering Chongqing University of Posts and Telecommunications China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science Chongqing University of Posts and Telecommunications Chongqing China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China Electric Power Institute CSG Guangzhou China

With the continuous deepening of China's power grid intelligent upgrading and the rise of edge computing technology, power grid terminals can carry out data storage and intelligent calculation at the edge of the power grid. However, the generated data by users may cause the leakage of user privacy information. The power grid edge system can retrieve the user's electricity data on demand, so as to improve the level of privacy protection and reduce the demand of transmission and computing resources through the localized storage of data. Therefore, how to carry out privacy protection and on-demand retrieval is a key problem to realize the intelligent power grid edge. In this paper, a multi-keyword cipher-text retrieval scheme (MCR- SGEC) for smart grid edge computing is proposed, which can achieve accurate matching of index and trap door, and has low spatial complexity. At the same time, on this basis, a multi-keyword fuzzy cipher-text retrieval method for smart grid edge computing (MFSE-SGEC) is proposed, which allows users to have spelling errors, which makes the user experience better.

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