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$\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy

IEEE Transactions on Information Forensics and security 2018年第9期13卷 2151-2166页

作者： Xuebin Ren Chia-Mu Yu Weiren Yu Shusen Yang Xinyu Yang Julie A. McCann Philip S. Yu School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Taiwan Information Security Center Taipei Taiwan Department of Computer Science and Engineering National Chung Hsing University Taichung Taiwan Department of Computing Imperial College London London U.K. School of Engineering and Applied Science Aston University Birmingham U.K. Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Department of Computer Science University of Illinois at Chicago Chicago IL USA

High-dimensional crowdsourced data collected from numerous users produces rich knowledge about our society; however, it also brings unprecedented privacy threats to the participants. Local differential privacy (LDP), a variant of differential privacy, is recently proposed as a state-of-the-art privacy notion. Unfortunately, achieving LDP on high-dimensional crowdsourced data publication raises great challenges in terms of both computational efficiency and data utility. To this end, based on the expectation maximization (EM) algorithm and Lasso regression, we first propose efficient multi-dimensional joint distribution estimation algorithms with LDP. Then, we develop a local differentially private high-dimensional data publication algorithm (LoPub) by taking advantage of our distribution estimation techniques. In particular, correlations among multiple attributes are identified to reduce the dimensionality of crowdsourced data, thus speeding up the distribution learning process and achieving high data utility. Extensive experiments on real-world datasets demonstrate that our multivariate distribution estimation scheme significantly outperforms existing estimation schemes in terms of both communication overhead and estimation speed. Moreover, LoPub can keep, on average, 80% and 60% accuracy over the released datasets in terms of support vector machine and random forest classification, respectively.

关键词： Privacy Data privacy Servers Correlation Estimation Frequency estimation Sensors

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TPEFD： an SDN-based efficient elephant flow detection method

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网络与信息安全学报 2017年第5期3卷 70-76页

作者： TIAN YU LIU Jing LAI Ying-xu BAO Zhen-shan ZHANG Wen-bo Faculty of Information Technology Beijing University of Technology Beijing 100124 China Key Laboratory of Trusted Computing Beijing 100124 China Information Security Rank Protection Key Technology National Engineering Laboratory Beijing 100124 China)

Software-defined networking(SDN) is a new approach to configure and operate programmable switches of the networks(especially the data center networks) through a centralized software controller. Elephant flows normally exist in data center networks and take up a large amount of network bandwidth, so the elephant flow detection is very important to ease network congestion. A two-phase real-time detection(TPEFD) method was proposed to detect the elephant flows in the SDN-based network. First, the controller obtained aggregated statistics and shrank sample scope until it was small enough, packets were sampled in the scope in switches. In order to identify the elephant flows, the sFlow sampling results were compared with the dynamic threshold. If the sampling value exceeded the threshold value, the flow was recognized as an elephant flow. The efficiency of our method in an SDN experimental environment was evaluated. The experimental results indicated that the proposed method was feasible and the detection time was efficient.

关键词： OpenFlow 象现金流量流度检波数据中心

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Collaboration Patterns at Scheduling in 10 Years 14th

Collaboration Patterns at Scheduling in 10 Years

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14th International Conference on Cooperative Design, Visualization, and Engineering, CDVE 2017

作者： Xu, Xiujuan Liu, Yu Ma, Ruixin Sheng, Quan Z. School of Software Dalian University of Technology Dalian116620 China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China Department of Computing Macquarie University SydneyNSW2109 Australia

ISBN: (纸本)9783319668048

Scheduling analysis, which focuses on the evaluation, testing and verification of the scheduling systems and the algorithms used in real-time operations, is critical to a number of research areas such as databases and transaction management. In order to better understand the field of scheduling, we select and investigate 9,611 papers about scheduling from five SCI journals which have published many excellent papers about scheduling. This paper presents a collaboration analysis of the scheduling field. In addition, we generate three networks to analyze collaboration patterns between scientists, including co-authorship network, keyword co-occurrence network and author co-keyword network to show the collaboration relationship between authors in the field of scheduling. The research findings from our work can help researchers understand the research status of scheduling and gain valuable insights on future technical trends in the scheduling field. © 2017, Springer International Publishing AG.

关键词： Scheduling

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Capacity optimality of AMP in coded systems

arXiv

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arXiv 2019年

作者： Liu, Lei Liang, Chulong Ma, Junjie Ping, Li Hong Kong SAR China Nomi 923-1292 Japan Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China Algorithm Department ZTE Corporation Shenzhen 518057 China State Key Laboratory of Mobile Network Mobile Multimedia Technology ZTE Corporation Shenzhen 518057 China Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Science Beijing China Department of Electronic Engineering City University of Hong Kong Hong Kong SAR China

This paper studies a large random matrix system (LRMS) model involving an arbitrary signal distribution and forward error control (FEC) coding. We establish an area property based on the approximate message passing (AMP) algorithm. Under the assumption that the state evolution for AMP is correct for the coded system, the achievable rate of AMP is analyzed. We prove that AMP achieves the constrained capacity of the LRMS with an arbitrary signal distribution provided that a matching condition is satisfied. As a byproduct, we provide an alternative derivation for the constraint capacity of an LRMS using a proved property of AMP. We discuss realization techniques for the matching principle of binary signaling using irregular low-density parity-check (LDPC) codes and provide related numerical results. We show that the optimized codes demonstrate significantly better performance over un-matched ones under AMP. For quadrature phase shift keying (QPSK) modulation, bit error rate (BER) performance within 1 dB from the constrained capacity limit is observed. © 2019, CC BY-NC-SA.

关键词： Message passing

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Hierarchical taxonomy-aware and attentional graph capsule RCNNs for large-scale multi-label text classification

arXiv

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arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Wang, Senzhang He, Lifang Li, Bo Wang, Lihong Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 State Key Laboratory of Software Development Environment Beihang University Beijing100083 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Biostatistics and Epidemiology University of Pennsylvania PhiladelphiaPA19104 United States National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics, but how to consider them both coherently is less studied. In addition, most existing methods treat output labels as independent medoids, but ignore the hierarchical relations among them, leading to useful semantic information loss. In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification. Specifically, we first propose to model each document as a word order preserved graph-of-words and normalize it as a corresponding words-matrix representation which preserves both the non-consecutive, long-distance and local sequential semantics. Then the words-matrix is input to the proposed attentional graph capsule recurrent CNNs for more effectively learning the semantic features. To leverage the hierarchical relations among the class labels, we propose a hierarchical taxonomy embedding method to learn their representations, and define a novel weighted margin loss by incorporating the label representation similarity. Extensive evaluations on three datasets show that our model significantly improves the performance of large-scale multi-label text classification by comparing with state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Detecting Phishing Websites via Aggregation Analysis of Page Layouts

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Procedia Computer Science 2018年 129卷 224-230页

作者： Jian Mao Jingdong Bian Wenqian Tian Shishi Zhu Tao Wei Aili Li Zhenkai Liang School of Electronic and Information Engineering Beihang University Beijing 100183 China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai 200240 China Baidu USA LLC. Sunnyvale CA 94089 USA Information Technology Service Center China National Petroleum Corporation Beijing 100007 China School of Computing National University of Singapore Singapore 117417 Singapore

Phishing websites are typical starting points of online social engineering attacks, including many recent online scams. The attackers develop web pages mimicking legitimate websites, and send the malicious URLs to victims to lure them to input their sensitive information. Existing phishing defense mechanisms are not sufficient to detect with new phishing attacks. In this paper, we aim to improve phishing detection techniques using machine learning techniques. In particular, we propose a learning-based aggregation analysis mechanism to decide page layout similarity, which is used to detect phishing pages. Our experiment results shows that our approach is accurate and effective in detecting phishing pages.

关键词： Anti-phishing Web security Machine learning Aggregation analysis

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Data-Driven Traditional Chinese Medicine Clinical Herb Modeling and Herb Pair Recommendation

Data-Driven Traditional Chinese Medicine Clinical Herb Model...

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International Conference on Digital Home (ICDH)

作者： Gansen Zhao Xutian Zhuang Xinming Wang Weimin Ning Zijing Li Jianfei Wang Qiang Chen Zefeng Mo Bingchuan Chen Huiyan Chen School of Computer Science Guangzhou Key Laboratory of Cloud Computing Security and Assessment Technology Dongguan Hospital of Traditional Chinese Medicine Guangdong University of Education School of Mathematical Sciences South China Normal University School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou Flaming Interactive Information Technology Co. LTD Guangdong China

As an important branch of medical field, Traditional Chinese Medicine(TCM) continues to be explored in data mining research. Taking advantage of machine learning models and deep learning methods, researchers dive into symptom analysis, disease prediction and medicine law. The combination of TCM herbs is the essential basis for compatibility of clinical prescriptions and its research has attracted plenty of attention. However, literature on herb recommendation for clinical diagnosis, to our best knowledge, is slightly lacking. The clinical herbs collocation will be chosen by doctors in consideration of not only the characteristics and pharmacodynamics of the herbs, but also the mutual effects formed with other herbs. Based on the real clinical prescription data, this paper constructs an analytical model to represent the relationship between prescription herbs and syndromes, and develops herb recommendation model. Firstly, by constructing a modeling process based on the LDA topic model, this paper shows the analysis model and presentation method for prescription herbs. Then, based on the mentioned modeling, we propose a double-end fusion recommendation framework, including methods of adjusting weight proportion and similarity remapping. This research conducts experiments on relevant outpatient medical record data, which confirm that the proposed model can reflect the basic principles of herb combination in clinical diagnosis and the proposed fusion recommendation model has good performance in evaluation metrics.

关键词： Data models Medical diagnostic imaging Analytical models Hidden Markov models Diseases Data mining

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Leveraging geometric correlation for input-adaptive facial landmark regression

Leveraging geometric correlation for input-adaptive facial l...

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2017 IEEE International Conference on Multimedia and Expo, ICME 2017

作者： Feng, Yuyao Liu, Risheng Fan, Xin Huyan, Kang Luo, Zhongxuan DUT-RU International School of Information and Software Engineering Dalian University of Technology China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781509060672

Facial analysis plays very important role in many vision applications, such as authentication and entertainments. The very early works in the 1990s mostly focus on estimating geometric deformations of facial landmarks to address this task. While in the past several years, more and more efforts have been made to directly learn an appearance regression for facial analysis. Though training regressions on controlled facial images can successfully capture the appearance variations, the performance of these appearance-based models are tightly related to the quantity and quality of the training data. In this paper, we develop a novel framework, named geometric correlated landmark regression (GCLR), to inherit the advantages but overcome limitations of these two categories of methods. Specifically, we first establish a landmark-to-landmark regression to estimate the geometry of facial images. By further incorporating a sparse coding term into the regression framework, we can successfully leverage the geometric correlations between the test image and the shape dictionary, thus significantly enhance the geometry regression performance. Experimental results on various challenging facial data sets verify the effectiveness and efficiency of GCLR. © 2017 IEEE.

关键词： Geometry

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Revisiting Recent and Current Anomaly Detection based on Machine Learning in Ad-Hoc networks

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Journal of Physics: Conference Series 2019年第1期1288卷

作者： Zhixiao Wang Mingyu Chen Wenyao Yan Wendong Wang Ang Gao Gaoyang Nie Feng Wang Shaobo Yang Xi'an University of Technology Xi'an 710048 China Goettingen University Goettingen 37077 Germany Xi'an Jiaotong University Xi'an 710049 China Shaanxi Provincial Key Laboratory of Network Computing and Security Technology Xi'an 710048 China College of Xi'an Innovation Yan'an University Xi'an 710100 China Yan'an University Yan'an 716000 China National Satellite Meteorological Center China Meteorological Administration Beijing 100081 China

Ad-Hoc network which is one kind of self-organized networks is much more vulnerable than the infrastructural network with the properties of highly changeable linkage, dynamic structure, and wireless connections so that the tradition intrusion detection system (IDS) should be improved to adapt in such network with limited computing resources and open channels. To ensure the security in Ad-Hoc network, the efficient anomaly detection methods should be probed. Over the past years, many studies have implemented anomaly detection methods (intrusion detection techniques) based on machine-learning methods in this field. This article analyzes the existing security problem in Ad-Hoc network, presents the basic theory of intrusion detection for Ad-Hoc network, and reviews the current and recent anomaly detection methods used machine learning techniques in the intrusion detection system.

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An SVEIR defending model with partial immunization for worms

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International Journal of network security 2017年第1期19卷 20-26页

作者： Wang, Fangwei Gao, Hongfeng Yang, Yong Wang, Changguang College of Information Technology Hebei Normal University No. 20 South ErHuan Road YuHua District Shijiazhuang050024 China Shaanxi Key Laboratory of Network and System Security Xidian University No. 2 TaiBai South Road YanTa District Xi'an710071 China Network and Information Center Guizhou University No. 242 Huaxi Street HuaXi District Guiyang550025 China Network and Information Center Yunnan University No. 2 Cuihu North Road WuHua District Kunming650091 China

Internet worms can propagate across networks horrendously, reduce network security remarkably, and cause economic losses heavily. How to quickly eliminate the Internet worms using partial immunization becomes a big issue for sustaining Internet infrastructure smoothly. This paper addresses this issue by presenting a novel worm attack model through incorporating a saturated incidence rate and a partial immunization rate, named SVEIR model. Using the basic reproduction number, we derive the global stability of the infection-free equilibrium and local stability of the unique endemic equilibrium. Numerical methods are employed to solve and simulate the developed system and also verify the proposed SVEIR model. Simulation results show that the partial immunization is highly effective for eliminating worms.

关键词： Immunization

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