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Local anatomy for personalised privacy protection

International Journal of Information and Computer Security 2021年第2-3期15卷 254-271页

作者： Li, Boyu Liu, Yanheng Wang, Minghai Sun, Geng Li, Bin College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China Big Data and Network Management Center Jilin University Changchun China College of Mathematics Jilin University Changchun China

Anonymisation technique has been extensively studied and widely applied for privacy-preserving data publishing. However, most existing methods ignore personal anonymity requirements. In these approaches, the microdata consist of three categories of attribute: explicit-identifier, quasi-identifier and sensitive attribute. In fact, the data sensitivity should be determined by individuals. An attribute is semi-sensitive if it contains both QI and sensitive values. In this paper, we propose a novel anonymisation approach, called local anatomy, to address personalised privacy protection. Local anatomy partitions the tuples who consider the value as sensitive into buckets inside each attribute. We conduct some experiments to illustrate that local anatomy can protect all the sensitive values and preserve great information utility. Additionally, we also present the concept of intelligent anonymisation system as our direction of future work. © 2021 Inderscience Enterprises Ltd.

关键词： Sensitive data

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An algorithm for min-cut density-balanced partitioning in P2P web ranking

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2015年 361卷 257-266页

作者： Sangamuang, Sumalee Boonma, Pruet Natwichai, Juggapong Data Engineering and Network Technology Laboratory Department of Computer Engineering Chiang Mai University Chiang Mai Thailand

In P2P-based PageRank computing, each computational peer contains a partitioned local web-link graph and its PageRank is computed locally. Then, collaborative web ranking between any two peers will be proceeded iteratively to adjust the web ranking until converge. In this paper, the problem of partitioning web-link graph for web ranking in P2P is formulated as a minimal cut-set with density-balanced partitioning. Then, an efficient algorithm called DBP-dRanking is proposed to address such problem. The algorithm can solve the problem with computational complexity of a polynomial function to the web-link graph size. The results also confirm that the proposed algorithm can reduce the ranking error by partitioning web-link graph and perform faster than two other algorithms. © Springer International Publishing Switzerland 2015.

关键词： Problem solving

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Policy network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud networks

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China Communications 2024年第4期21卷 53-73页

作者： Feng Chuan Zhang Xu Han Pengchao Ma Tianchun Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communication and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China

The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/***, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.

关键词： benefit maximization deep reinforcement learning multi-access edge cloud task offloading

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Dual-Threshold Personalized Federated Learning Method Based on Blockchain 4

Dual-Threshold Personalized Federated Learning Method Based ...

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4th International Conference on Blockchain technology and Information Security, ICBCTIS 2024

作者： Liu, Wei Wang, Ying Cui, Wentao She, Wei Tian, Zhao School of Cyber Science and Engineering Zhengzhou University Henan Key Laboratory of Network Cryptography Technology Zhengzhou Key Laboratory of Blockchain and Data Intelligence Zhengzhou China School of Cyber Science and Engineering Zhengzhou University Zhengzhou China School of Cyber Science and Engineering Zhengzhou University SongShan Laboratory Zhengzhou China School of Cyber Science and Engineering Zhengzhou University Zhengzhou Key Laboratory of Blockchain and Data Intelligence Zhengzhou China

ISBN: (纸本)9798331528324

Federated learning has emerged as the forefront of research in recent years. However, its distributed framework poses a risk of a single point of failure in data research. Moreover, distinguishing malicious clients in the process of realizing personalized federated learning through client similarity is a critical concern. To overcome the aforementioned problems, we recommend a blockchain-based, dual-threshold personalized federated learning approach. Blockchain is used to replace the central server in the traditional federated learning framework. And we design a double-threshold-based malicious client screening algorithm, which achieves final malicious client identification by establishing screening criteria on both the blockchain side and the client side. The experimental findings demonstrate that the suggested technique maintains good accuracy and convergence even in the presence of malicious clients, compared to the FedAvg algorithm. © 2024 IEEE.

关键词： Federated learning

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Effects of Optimized Operating Parameters on Combustion Characteristics and NO_(x)Emissions of a Burner based on Orthogonal Analysis

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Journal of Thermal Science 2021年第4期30卷 1212-1223页

作者： XU Qian AKKURT Nevzat YANG Gang ZHU Lidong SHI Kejian WANG Kang ZOU Zhenwei LIU Zhihui WANG Jiulong DU Zhiwei School of Energy and Environmental Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory for Energy Conservation and Emission Reduction for Metallurgical Industry Beijing 100083China Department of Mechanical Engineering Munzur UniversityTunceli 62000Turkey Department of Big Data Technology and Application Development Computer Network Information CenterChinese Academy of SciencesBeijing 100190China AVIC Xinxiang Aviation Industry(group)CO. LTDHenan 453002China

To optimize the structure of the burner,improve the combustion performance,and reduce the emission of NO_(x),a self-circulating low NO_(x)combustion technology was used to design a new type of flue gas self-circulating low NO_(x)*** on previous research on the numerical model of combustion and the composition of mixed gas on combustion and NO_(x)emissions,the effect of various factors on the ejection coefficient of the flue gas self-circulating structure was analyzed using the orthogonal test method,and the burner operating parameters,such as preheating temperature and excess air coefficient,were deeply studied through the three-dimensional finite element numerical model in this *** results show that the diameter ratio of the nozzle and the length of the cylindrical section of the flue gas self-circulating structure have great influence on its ejection and mixing *** optimal ejection coefficient was ***,the amount of NO_(x)emissions greatly increased from 6.23×10^(-6)(volume fraction)at the preheating temperature 973 K to 3.5×10^(-3)at preheating temperature 1573 *** the excess air coefficient decreased from 1.2 to 1,the maximum combustion temperature decreased from 2036.3 K to 1954.22 K,and the NO_(x)emissions decreased from 352.29×10^(-6)to 159.73×10^(-6).

关键词： preheating temperature excess air coefficient self-circulating flue gas low oxygen combustion low-NO_(x)emissions

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Thematic Event Extraction Based on Event-Related Sentence Detection 1

Thematic Event Extraction Based on Event-Related Sentence De...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Zhang, Xing Guo, Yucan Jin, Xiaolong Liang, Haozhe Guan, Saiping School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Sciences Cas Key Laboratory of Network Data Science and Technology China Institute of Systems Engineering Academy of Military Science Australia

ISBN: (纸本)9798350358780

Existing works in event extraction typically extract event arguments within the sentence scope. However, besides the sentence level, events may also be naturally presented at the document level. A document-level event usually reflects, to some extent, the theme (i.e., the main content) of the document (e.g., electronic medical records and news articles), which is thus referred to as the thematic event. Thematic Event Extraction (TEE) aims to extract the arguments of thematic events. TEE faces a major challenge, i.e., the sparsity and dispersion of arguments, which means that the arguments of a thematic event are dispersed in different sentences of the document. To overcome this challenge, we propose an Event-related Sentence Detection based TEE model, called ESDTEE, which first detects the sentences related to the thematic event and then extracts the arguments only within these detected sentences using existing models. Extensive experiments with comprehensive analyses demonstrate the effectiveness of ESDTEE. © 2023 IEEE.

关键词： Medical computing

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Alert correlation algorithms: A survey and taxonomy

arXiv

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arXiv 2018年

作者： Mirheidari, Seyed Ali Arshad, Sajjad Jalili, Rasool Data and Network Security Laboratory Department of Computer Engineering Sharif University of Technology

Alert correlation is a system which receives alerts from heterogeneous Intrusion Detection Systems and reduces false alerts, detects high level patterns of attacks, increases the meaning of occurred incidents, predicts the future states of attacks, and detects root cause of attacks. To reach these goals, many algorithms have been introduced in the world with many advantages and disadvantages. In this paper, we are trying to present a comprehensive survey on already proposed alert correlation algorithms. The approach of this survey is mainly focused on algorithms in correlation engines which can work in enterprise and practical networks. Having this aim in mind, many features related to accuracy, functionality, and computation power are introduced and all algorithm categories are assessed with these features. The result of this survey shows that each category of algorithms has its own strengths and an ideal correlation frameworks should be carried the strength feature of each category. Copyright © 2018, The Authors. All rights reserved.

关键词： network security

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Intelligence’s Dilemma: AI Algorithms May Cause Anti-Intelligence

Intelligence’s Dilemma: AI Algorithms May Cause Anti-Intell...

引用

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

作者： Zhang, Yuhong Nauman, Umer Network and Data Security Key Laboratory of Sichuan University of Electronic Science and Technology of China Chengdu610054 China Information Science and Engineering College Henan University of Technology Zhengzhou450001 China

ISBN: (纸本)9789811379826

As the intelligent age continues and technology gets more advanced, algorithms offer some of the solutions for our better work and life. However, research suggests that the proliferation and abuse of intelligent algorithms may lead us to a decline in cognitive performance. In this paper, we first discuss the role of intelligent algorithms in terms of highlighting the value of data. Next, we analyse the non-neutrality of these data-driven algorithms. Further, we analyze the intrinsic discrimination of AI algorithm based on prejudice psychology. Next, we argue that overusing AI algorithms and strengthening the dependency on them will lead to lower our cognitive level in terms of slow thinking. Thus, the conclusion is that, in the intelligent age, the more intelligent algorithms we use, the lower our cognitive level will be. This is the dilemma of intelligence. Finally, we offer some possible principles that may apply to alleviate the above disadvantageous scenarios. © 2019, Springer Nature Singapore Pte Ltd.

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Modeling of unsupervised knowledge graph of events based on mutual information among neighbor domains and sparse representation

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Defence technology（防务技术） 2022年第12期18卷 2150-2159页

作者： Jing-Tao Sun Jing-Ming Li Qiu-Yu Zhang School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China School of Management Science and Engineering Anhui University of Finance and EconomicsBengbuAnhui230030China School of Computer and Communication Lanzhou University of TechnologyLanzhouGansu730050China

Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,***,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of ***,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature ***,the events knowledge graph is constructed by means of sparse representation and l1 *** experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding *** experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery.

关键词： Text event mining Knowledge graph of events Mutual information among neighbor domains Sparse representation

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Service Decision Mechanism Based On Traffic Flow Prediction in the Internet of Vehicles 23

Service Decision Mechanism Based On Traffic Flow Prediction ...

引用

23rd IEEE International Conference on Communication technology, ICCT 2023

作者： Hu, Yanzhe Feng, Chenyuan Jiang, Wei Shi, Wenzhe Feng, Daquan Shenzhen University Shenzhen Key Laboratory of Digital Creative Technology Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China College of Information Engineering Zhejiang University of Technology Zhejiang Hangzhou China Zte Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology China

ISBN: (纸本)9798350325959

The service time of Road-Side Units (RSUs) for vehicle users will directly affect the Quality of Service (QoS) in the Internet of Vehicles (IoV), and it is related to the traffic flow and service decisions. Consequently, a sophisticated service decision mechanism based on the traffic flow prediction is necessary beforehand in order to optimize QoS. In this work, we propose a service decision based on traffic flow prediction to reduce the service waiting time of vehicles. Firstly, we propose a traffic flow prediction model based on deep learning. Convolutional Neural network (CNN) is used to capture spatial data in order to handle the complicated road network layout in metropolitan roads. First, to reveal people's travel patterns, temporal features are captured by two different models. While short-term features are captured from historical data by Auto-Encoder, daily period feature as well as weekly period feature are captured from bi-directional data by Bidirectional Long Short-Term Memory (Bi-LSTM). Second, by simulating the process of RSUs serving vehicles using a queuing theory model, we propose a service decision mechanism based on the anticipated traffic flow. The proposed service decision mechanism aims to reduce the average waiting time of vehicles for service. Particularly, the RSUs will ask the Macro Base Station (MBS) for assistance when there are too many vehicles traveling through the RSUs. Finally, we conduct comprehensive experiments using real data to evaluate the model performance, and it is demonstrated that our proposed scheme outperforms other benchmarks. © 2023 IEEE.

关键词： Internet of Vehicles queuing theory service decision mechanism traffic flow prediction

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