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Safeguarding Privacy in Real-Time Trajectory Releases for AI-Driven Consumer Applications

IEEE Transactions on Consumer Electronics 2025年

作者： Cai, Sujin Tian, Wei Lv, Taizhi Wang, Xiangchen Li, Xin Jiangsu Maritime Institute School of Information and Engineering Nanjing211106 China Guizhou University of Commerce College of Computer and Information engineering Guiyang550014 China Southern University of Science and Technology College of Computer Science Shenzhen518055 China Hohai University College of Computer Science and Software Engineering Nanjing211106 China

Intelligent consumer applications optimize product recommendations by analyzing users' real-time trajectory information and mobility patterns to gain insights into their consumption preferences. However, privacy concerns often cause individuals to hesitate when sharing their locations. This paper introduces a real-time trajectory release scheme called Dynamic Trajectory Releasing based on Geo-indistinguishability (DTRG) that ensures client-side data protection. A significant contribution of this work is the introduction of a novel concept, w-event Geo-indistinguishability, which extends the measurement of privacy for real-time trajectories. Building upon the theoretical foundation of w-event Geo-indistinguishability, we design a dynamic privacy budget allocation strategy that doesn't require pre-setting trajectory length. This strategy recycles unused privacy budgets and progressively reallocating them in a backward cumulative manner, enhancing the utilization efficiency of the privacy budget. By incorporating users' travel information for prediction, DTRG adaptively perturbs real locations based on prediction results. Through the collaborative operation of the Prediction Mechanism (PM), Detection Mechanism (DM) and Noisy Mechanism (NM), this approach further optimizes the utility of the budget. Experimental results demonstrate that DTRG significantly improves data availability, achieving up to a 36% increase compared to some competing algorithms while maintaining the desired level of privacy. © 1975-2011 IEEE.

关键词： Differential privacy

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AI-Enhanced Multi-Cloud Security Management: Ensuring Robust Cybersecurity in Hybrid Cloud Environments

AI-Enhanced Multi-Cloud Security Management: Ensuring Robust...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Srimathi, J. Kanagasabapathi, K. Mahajan, Kirti Ahamad, Shahanawaj Soumya, E. Barthwal, Shivangi KPR College of Arts Science and Research Department of Information Technology Covai Arasur641407 India Karpagam College of Engineering Computer Science and Technology Coimbatore India Bharati Vidyapeeth Deemed University Institute of Management & Entrepreneurship Development Department of Information Technology Pune411030 India University of Hail College of Computer Science and Engineering Department of Software Engineering Hail City Saudi Arabia St. Martins Engineering College Department of Computer Science and Engineering Telangana Secunderabad India Graphic Era Deemed to Be University Department of Electronics & Communication Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350319200

The complex landscape of multi-cloud settings is the result of the fast growth of cloud computing and the ever-changing needs of contemporary organizations. Strong cyber defenses are of fundamental importance in this setting. In this study, we investigate the use of AI in hybrid cloud settings for the purpose of multi-cloud security management. To help businesses improve their productivity and resilience, we provide a mathematical model for optimal resource allocation. Our methodology streamlines dynamic threat assessments, making it easier for security teams to efficiently priorities vulnerabilities. The advent of a new age of real-time threat response is heralded by the incorporation of AI-driven security tactics. The technique we use has real-world implications that may help businesses stay ahead of constantly changing threats. In the future, scientists will focus on autonomous security systems, interoperability, ethics, interoperability, and cutting-edge AI models that have been validated in the real world. This study provides a detailed road map for businesses to follow as they navigate the complex cybersecurity landscape of multi-cloud settings, therefore promoting resilience and agility in this era of digital transformation. © 2023 IEEE.

关键词： Adaptive Security Systems Artificial Intelligence Autonomous Security Cybersecurity Hybrid Cloud Multi-Cloud Security Quantum Computing Real-Time Threat Response Threat Assessment

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Domain Contrastive Learning for Multi-Site Alzheimer's Disease Classification 20

Domain Contrastive Learning for Multi-Site Alzheimer's Disea...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Fiasam, Linda Delali Rao, Yunbo Sey, Collins Aggrey, Esther Stacy E. B. Kodjiku, Seth Larweh Browne, Judith Ayekai Danso, Juliana Mantebea Ukwuoma, Chiagoziem C. Gyarteng, Emmanuel S. A. School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Zhejiang Gongshang University China School of Computer Science and Technology University of Electronic Science and Technology of China University China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

ISBN: (纸本)9798350318982

In the field of medical imaging, accurately classifying Alzheimer's Disease (AD) poses a substantial challenge, primarily due to the inherent variability in imaging data across different clinical sites. This paper introduces a novel approach, Domain Contrastive Learning (DCL), to address this challenge by enhancing the robustness and transferability of AD classification models using multi-site Magnetic Resonance Imaging (MRI) data. DCL leverages a unique contrastive learning technique within an autoencoder's latent space, enabling the extraction of domain-invariant features essential for accurate diagnosis. We rigorously evaluate the proposed network on brain images from three distinct imaging sites, employing a Domain Generalization (DG) training protocol to ensure robustness and generalizability across varying data sources. Our model is evaluated on three binary classification tasks: AD vs. Normal Control (NC), AD vs. Mild Cognitive Impairment (MCI), and MCI vs. NC. Results demonstrate that DCL outperforms existing methods, especially in AD vs. NC classification, achieving higher accuracy, sensitivity, and specificity with 0.51%, 1.54% and 1.68% increments respectively. This underscores DCL's potential as a robust and generalizable method for enhanced AD diagnosis, marking a significant step forward in medical imaging challenges in a multi-site context. © 2023 IEEE.

关键词： Magnetic resonance imaging

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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A Sequential-based Deep Learning Model for Dry Beans Classification

A Sequential-based Deep Learning Model for Dry Beans Classif...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Sujatha, R. Chatterjee, Jyotir Moy Rohith, A. Ramadan, Rabie A. Vellore Institute of Technology Software and Systems Engineering Vellore India Information Technology Lord Buddha Education Foundation Kathmandu Nepal Cairo University Computer Engineering Giza Egypt College of Computer Science and Engineering Hail University Hail Saudi Arabia

ISBN: (纸本)9798350347050

People take various types of cereals every day in their regular meals, but most people do not know their importance while consuming them. Each cereal has its benefit. One such cereal content is dry beans. Dry beans are a variety of beans that are produced in pods. These dry beans can be cooked and eaten, and it has numerous proteins and vitamins and are highly beneficial for our health. It also provides excellent immunity. Each bean has its characteristics, which can be identified with its unique features. These can be oval or kidney-shaped or even without a proper shape. According to their shape and features, they are classified separately. These nutrient-rich beans have to be classified based on their shapes. Human eyes sometimes may oversee or misclassify these tiny cereals for classification. This work involves the classification of 7 such dry bean varieties utilizing a deep learning context. The dataset utilized in this paper includes 13611 dry bean data for seven different UCI Machine learning repositories. In this work, we have taken seven different categorical labels: the dry bean varieties. The classification is done using deep learning techniques, and here we have utilized the Keras Sequential Algorithm for the classification. It is a supervised learning concept of machine learning used to predict two or more categorical labels. With the help of the deep learning approach, these dry beans are classified and obtained an accuracy of 94.88%. © 2023 IEEE.

关键词： Deep learning

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Gamification of E-Learning Apps via Acceptance Requirements Analysis 19

Gamification of E-Learning Apps via Acceptance Requirements ...

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19th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2024

作者： Calabrese, Federico Piras, Luca Al-Obeidallah, Mohammed Ghazi Egbikuadje, Benedicta Oghenevoke Alkubaisy, Duaa Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Computer Science Middlesex University London United Kingdom Department of Software Engineering Al Ain University Abu Dhabi United Arab Emirates College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

ISBN: (纸本)9789897586965

In the last few years, and particularly during and after the COVID-19 pandemic, E-Learning has become a very important and strategic asset for our society, relevant both for academic and industry settings, involving participants ranging from students to professionals. Different applications have been developed to support E-Learning to be an effective tool, particularly in relation to the software engineering and programming areas. However, in order to be effective, in particular within academic settings, such tools require students to be continuously engaged and motivated to learn both practical and theoretical aspects. The integration of gamification in educational environments has gained considerable prominence as a potential mean to augment students’ motivation and involvement, providing them with immediate feedback and reinforcement, bolstering their sense of accomplishment and motivation to persist in their studies. However, to design gamified applications that can effectively engage and motivate users, as the literature has demonstrated, it is required to consider psychological, sociological, and human behavioural aspects, often referred to as Acceptance Requirements. This study presents a case study, where a Goal Modeling-based, Systematic, Acceptance Requirements Analysis and Gamification Design process, has been applied, by using the Agon framework, to prototype a gamified tool, aiming at engaging students towards learning both theory and practice related to a "Web-Based Mobile App Development" university module. To evaluate our proposed prototype, students were involved to use our proposed gamified prototype. The results show that our gamification solution can engage and motivate students towards learning both theoretical and practical aspects of the module. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Requirements engineering

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GCSS:a global collaborative scheduling strategy for wide-area high-performance computing

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Frontiers of computer science 2022年第5期16卷 1-15页

作者： Yao SONG Limin XIAO Liang WANG Guangjun QIN Bing WEI Baicheng YAN Chenhao ZHANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Smart City College Beijing Union UniversityBeijing 100101China

Wide-area high-performance computing is widely used for large-scale parallel computing applications owing to its high computing and storage ***,the geographical distribution of computing and storage resources makes efficient task distribution and data placement more *** achieve a higher system performance,this study proposes a two-level global collaborative scheduling strategy for wide-area high-performance computing *** collaborative scheduling strategy integrates lightweight solution selection,redundant data placement and task stealing mechanisms,optimizing task distribution and data placement to achieve efficient computing in wide-area *** experimental results indicate that compared with the state-of-the-art collaborative scheduling algorithm HPS+,the proposed scheduling strategy reduces the makespan by 23.24%,improves computing and storage resource utilization by 8.28%and 21.73%respectively,and achieves similar global data migration costs.

关键词： high-performance computing scheduling strategy task scheduling data placement

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Knowledge Graph-Based computer Aided Adaptive Learning 10

Knowledge Graph-Based Computer Aided Adaptive Learning

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10th International Conference on Information Systems and Computing Technology, ISCTech 2022

作者： Yi, Wenlong Song, Tingting Chen, Jie Zhao, Yingding Yin, Hua Xu, Yilu School of Software Jiangxi Agricultural University Nanchang China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer and Information Engineering Jiangxi Agricultural University Nanchang China

ISBN: (纸本)9798350332933

Although the teaching progress is easily controllable in traditional linear classroom teaching, the individual cognitive ability of the students is ignored in undifferentiated teaching, which easily leads to the mismatch between the teaching content and the learners' ability and makes them lose confidence in learning. The study proposes a computer-assisted method of the adaptive learning plan. First, the knowledge graph technology is used to represent the semantics of course knowledge points;second, a recommended algorithm for adaptive learning based on questionnaire scoring is designed;finally, Neo4j, Spring Boot and *** are used for programming. The experimental results showed that this method promoted the learners' self-adaptive learning, met the students' needs for differentiated learning, improved the overall teaching quality, and achieved the student-oriented teaching concept. © 2022 IEEE.

关键词： Knowledge graph

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Body Part Segmentation of Anime Characters

Body Part Segmentation of Anime Characters

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作者： Ou, Zhenhua Liu, Xueting Li, Chengze Wen, Zhenkun Li, Ping Gao, Zhijian Wu, Huisi College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing and Information Sciences Saint Francis University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong College of Electronic and Information Engineering Shenzhen University Shenzhen China

Semantic segmentation is an important approach to present the perceptual semantic understanding of an image, which is of significant usage in various applications. Especially, body part segmentation is designed for segmenting body parts of human characters to assist different editing tasks, such as style editing, pose transfer, and animation production. Since segmentation requires pixel-level precision in semantic labeling, classic heuristics-based methods generally have unstable performance. With the deployment of deep learning, a great step has been taken in segmenting body parts of human characters in natural photographs. However, the existing models are purely trained on natural photographs and generally obtain incorrect segmentation results when applied on anime character images, due to the large visual gap between training data and testing data. In this article, we present a novel approach to achieving body part segmentation of cartoon characters via a pose-based graph-cut formulation. We demonstrate the use of the acquired body part segmentation map in various image editing tasks, including conditional generation, style manipulation, pose transfer, and video-to-anime. © 2024 John Wiley & Sons Ltd.

关键词： Semantic Segmentation

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Introducing on-chain graph data to consortium blockchain for commercial transactions

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Frontiers of computer science 2024年第2期18卷 255-257页

作者： Pengyi ZHANG Yuchen YUAN Jie SONG Yu GU Qiang QU Yongjie BAI Software College Northeastern UniversityShenyang 110819China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Blockchain Lab Huawei Cloud Computing Technologies Co.Ltd.Shenzhen 518101China

1 Introduction A blockchain can be referred to as a distributed ledger[1].Nowadays,according to our previous work[2],the graphrelated blockchain grows efficiently in both academic studies and commercial *** data,algor... 详细信息

关键词： transactions ledge commercial

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