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Differential privacy–based location privacy enhancing in edge computing

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Differential privacy–based location privacy enhancing in ed...

作者： Miao, Qiucheng Jing, Weipeng Song, Houbing College of Information and Computer Engineering Northeast Forestry University Harbin China Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona Beach United States

In the era of edge computing, real-time data preprocessing on the edge node has the potential to improve computational efficiency and data accuracy. However, a significant challenge is private data disclosure, particularly in the case of location-based services. To address this challenge, in this paper, by leveraging differential privacy, we propose a privacy-aware framework for mobile edge computing called MEPA to protect the location privacy in which the edge node is regarded as an anonymous central server. The proposed framework can provide computing services without deploying special infrastructure. To be specific, in order to solve the problem of constrained computing resources in the edge nodes, the algorithm of Quadtree Differential Privacy based on Hilbert curve division (QTDP-H) two-dimensional spatial data query transmission is proposed. First, a noise quadtree is established and the privacy budget is divided according to the tree level. Then, the constructed quadtree is represented by quanternary, so that the partition based on Hilbert curve can be established and the two-dimensional data in the area can be converted into one-dimensional, which can greatly improve the retrieval efficiency. The effectiveness of the proposed algorithm in terms of time complexity and retrieval accuracy has been verified by extensive experimental results. Compared with traditional methods of (D,Ε) − LP, the average runtime can be reduced by 15%-20%, and the average relative error is reduced by 20%. © 2018 John Wiley & Sons, Ltd.

关键词： Location based services

A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation 2

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A Time Effect based Collaborative Filtering Approach for Use...

2019 2nd International Conference on computer Information Science and Artificial Intelligence, CISAI 2019

作者： Chen, Yuxi Zhang, Xiaotong Zhao, Qing Akhtar, Faheem School of Statistics and Mathematics Country Central University of Finance and Economics Beijing100081 China Harpur College of Arts and Sciences Binghamton University-SUNY New York13902 United States Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Vrije Universiteit Amsterdam1081 HV Netherlands School of Software Engineering Beijing University of Technology Beijing Engineering Research Center for IoT Software and Systems Beijing100124 China Department of Computer Science Sukkur IBA University Sukkur65200 Pakistan

With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user's behaviour, which means a user's interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn't consider the time effect;next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method. © Published under licence by IOP Publishing Ltd.

关键词： Collaborative filtering

Cloud storage architecture: Research challenges and opportunities

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arXiv 2020年

作者： Ghani, Anwar Badshah, Afzal Jan, Saeedullah Alshdadi, Abdulrahman A. Daud, Ali Department of Computer Science & Software Engineering International Islamic University Islamabad44000 Pakistan Department of Computer Science & IT University of Malakand Chakdara18800 Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Saudi Arabia

From home appliances to industrial enterprises, the Information and Communication Technology (ICT) industry is revolutionizing the world. We are witnessing the emergence of new technologies (e.g, Cloud computing, Fog computing, Internet of Things (IoT), Artificial Intelligence (AI) and Block-chain) which proves the growing use of ICT (e,g. business, education, health and home appliances), resulting in massive data generation. It is expected that more than 175 ZB data will be processed annually by 75 billion devices by 2025. The 5G technology (i.e. mobile communication technology) dramatically increases network speed, enabling users to upload ultra high definition videos in real-time, will generate a massive stream of big data. Furthermore, smart devices, having artificial intelligence, will act like a human being (e.g, a self-driving vehicle etc) on the network, will also generate big data. This sudden shift and massive data generation created serious challenges in storing and managing heterogeneous data at such a large scale. This article presents a state-of-the-art review of the issues and challenges involved in storing heterogeneous big data, their countermeasures (i.e, from security and management perspectives), and future opportunities of cloud storage. These challenges are reviewed in detail and new dynamics for researchers in the field of cloud storage are discovered. Copyright © 2020, The Authors. All rights reserved.

关键词： Cloud security

Regressor driven incremental instance segmentation for contraband items identification

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engineering Applications of Artificial Intelligence 2025年 157卷

作者： Ammara Nasim Mahboob Anwar Muhammad Owais Samet Akcay Divya Velayudhan Muhammad Usman Akram Mohammed Ghazal Naoufel Werghi Taimur Hassan Department of Computer and Software Engineering National University of Sciences and Technology (NUST) Islamabad 44000 Pakistan Department of Electrical Computer and Biomedical Engineering Abu Dhabi University Abu Dhabi 59911 United Arab Emirates Khalifa University Center for Autonomous Robotic Systems (KUCARS) Khalifa University Abu Dhabi 127788 United Arab Emirates Research and Development Division Intel Corporation United Kingdom

Detecting suspicious objects contained within passenger baggage is one of the most difficult tasks, even for the security experts. To address this problem, many researchers have developed computer-aided screening methods employing deep learning models to detect the presence of contraband items using X-ray imagery. However, all of these models have limitations or bottlenecks when it comes to detecting prohibited objects that are heavily obscured, cluttered, overlapping, and well concealed within the baggage. To overcome these challenges, we present a novel instance segmentation framework that transforms the conventional semantic segmentation models to perform instance-aware segmentation, via incremental learning, to automatically detect suspicious baggage items. This framework leverages knowledge distillation, enabling the model to iteratively learn and retain multiple instances of threat items. By continuously adapting, the model improves its ability to distinguish between different instances, showcasing significant advancements in security and threat detection. To overcome the under-segmentation and over-segmentation problem of the incremental instance segmentation, we introduced the use of a lightweight regressor model that can accurately identify the overlapping instances of the suspicious objects which enables the optimal selection of the segmentation model to perform the required task. Moreover, the proposed framework has been rigorously tested on two publicly available datasets on which it achieved the mask mean average precision scores of 0.54 and 0.51, respectively, at the inference stage. Similarly, the proposed method outperformed the state-of-the-art methods by 5.88%, and 8.51% in terms of mask mean average precision scores across both datasets, respectively. In addition to this, the proposed framework provides an optimal trade-off between performance and efficiency as compared to its competitors.

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Prediction of global spread of covid-19 pandemic: A review and research challenges

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TechRxiv

TechRxiv 2020年

作者： Shah, Saloni Mulahuwaish, Aos Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Department of Computer Science and Information Systems Saginaw Valley State University MI United States Department of Software Engineering Salahaddin University Erbil Iraq School of Mathematics and Computer Science University of Wolverhampton Wolverhampton United Kingdom Department of Software Engineering Faculty of Engineering Koya University Kurdistan Iraq

Since the initial reports of the Coronavirus surfacing in Wuhan, China;the novel virus currently without a cure has spread like a wildfire across the globe. The virus spread exponentially across all inhabited continent;catching local governments by surprise in many cases and bringing the world economy to a standstill. As local authorities work on a response to deal with the virus, the scientific community has stepped in to help analyse and predict the pattern and conditions that would influence the spread of this unforgiving virus. Using existing statistical modelling tools to latest AI technology;the scientific community has used public and privately available data to help with predictions. A lot of this data research has enabled local authorities to plan their response – whether that is to deploy tightly available medical resources like ventilators or how and when to enforce policies to social distance including lockdowns. On one hand, this paper shows what accuracy of research brings to enable fighting this disease;while on the other hand it also shows what lack of response from local authorities can do in spreading this virus. This is our attempt in compiling different research methods and comparing their accuracy in predicting the spread of COVID-19. © 2020, CC BY.

关键词： Viruses

BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer’s Disease

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arXiv 2023年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung The Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China The Department of Systems and Enterprises Stevens Institute of Technology United States School of Science and Technology Hong Kong Metropolitan University Hong Kong College of Engineering and Computer Science VinUni-Illinois Smart Health Center VinUniversity Viet Nam Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Interdisciplinary Research Center for Biosystems and Machines King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Game Design Faculty of Arts Uppsala University Sweden The School of Software Tiangong University Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer’s disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. Copyright © 2023, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

AN IMPROVED GENERIC ER SCHEMA FOR CONCEPTUAL MODELLING OF INFORMATION systems

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arXiv 2020年

作者： Pieris, D. Wijegunasekera, M.C. Dias, N.G.J. Department of HRM Faculty of Commerce and Management Studies Department of Software Engineering Faculty of Computing and Technology Department of Computer Systems Engineering Faculty of Computing and Technology University of Kelaniya Sri Lanka

The Entity Relationship (ER) model is widely used for creating ER schemas for modelling application domains in the field of Information systems development. However, when an ER schema is transformed to a Relational Database Schema (RDS), some important information on the ER schema may not be represented meaningfully on the RDS. This causes loss of information during the transformation process. Although, several previous researches have proposed solutions to remedy the situation, the problem still exists. Thus, in this on-going research we wish to improve the proposed solutions and maximize information preservation in the ER to relational transformation process. Cardinality ratio constraints, role names, composite attributes, and certain relationship types are among the information frequently lost in the transformation process. Deficiencies in the ER model and the transformation method seems causing this situation. We take the view that if the information lost is resolved;a one-to-one mapping should exist from the ER schema to its RDS. We modified the ER model and the transformation algorithm following a heuristic research method with a view to eliminating the deficiencies and thereby achieving a one-to-one mapping. We should show that the mapping exists for any real world application. We create a generic ER schema - an ER schema that represents any phenomena in symbolic form - and use it to show that a one-to-one mapping exists for any real world application. In this paper, we explore our generic ER schema and its advantages over its predecessors in view of representing any real world application. Copyright © 2020, The Authors. All rights reserved.

关键词： Information use

Feature importance in mobile malware detection

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arXiv 2020年

作者： Kouliaridis, Vasileios Kambourakis, Georgios Peng, Tao Department of Information & Communication Systems Engineering University of the Aegean Greece School of Computer Science and Educational Software Guangzhou University Guangzhou China

The topic of mobile malware detection on the Android platform has attracted significant attention over the last several years. However, while much research has been conducted toward mobile malware detection techniques, little attention has been devoted to feature selection and feature importance. That is, which app feature matters more when it comes to machine learning classification. After succinctly surveying all major, dated from 2012 to 2020, datasets used by state-of-the-art malware detection works in the literature, we analyse a critical mass of apps from the most contemporary and prevailing datasets, namely Drebin, VirusShare, and AndroZoo. Next, we rank the importance of app classification features pertaining to permissions and intents using the Information Gain algorithm for all the three above-mentioned datasets. © 2020, CC BY.

关键词： Feature extraction

Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques

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arXiv 2025年

作者： Hasan, Mahade Yasmin, Farhana Hassan, Md Mehedi Yu, Xue Yeasmin, Soniya Joshi, Herat Islam, Sheikh Mohammed Shariful School of Software Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Technology Nanjing University of Information Science and Technology Nanjing China Computer Science and Engineering Discipline Khulna University Khulna Bangladesh Department of Computer Science and Engineering North Western University Khulna Bangladesh Great River Health Systems BurlingtonIA United States Institute for Physical Activity and Nutrition Deakin University MelbourneVIC Australia

Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. Our approach involved feature selection techniques to identify the most relevant predictors, aimed at refining the models to enhance both performance and interpretability. The models were trained, incorporating processes such as grid search hyperparameter tuning, and cross-validation to minimize overfitting. Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). Among the models, XGBoost demonstrated exceptional performance, achieving 99% accuracy, precision, F1-Score, 98% recall, and 100% ROC AUC. This study offers a promising approach to early heart disease diagnosis and preventive healthcare. © 2025, CC BY.

关键词： Support vector machines