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检索条件"任意字段=Proceedings of the 5th ACM Conference on Data and Application Security and Privacy"

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5th International conference on Computing and Network Communications, CoCoNet 2023

5th International Conference on Computing and Network Commun...

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5th International conference on Computing and Network Communications, CoCoNet 2023

ISBN: (纸本)9789819747108

the proceedings contain 44 papers. the special focus in this conference is on Computing and Network Communications. the topics include: Maximizing Efficiency: Unveiling the Potential of Kubernetes Metrics;resource-Aware Learning Automata for Low-Latency Task Scheduling in Fog Computing;Design of High Gain Wideband Petal-Shaped Magneto-Electric Dipole Metal Antenna for 5G Sub-6 GHz and Wi-Fi applications;A Novel Billhook-Shaped 2-Element MIMO Array for Sub-6 GHz Millimeter Wave application;energy-Efficient Transmission in Wireless Sensor Network Using Compressive Sensing;smart City Solutions for Traffic Management;an IoT-Assisted Real-Time Urban Air Pollution data Collection System;real-Time Stock Price Prediction Using Apache Kafka;RSS-Based Localization Using GRU and BiGRU Deep Learning Models in LoRaWAN-IoT Networks;locating and Routing for IoT-Based Fire Evacuation System;an Energy-Efficient data Management Approach Using Sparse Compression in Future-Ready IoT Networks;a Framework for Developing IoT and Edge Computing-Based Smart Transportation applications;a Mathematical Model for data Traffic Management in Next-Generation IoT Networks;crowdsourced Mobile applications for Disaster Relief: A Case Study of Landslide Tracker and Amrita Kripa App Deployments in Sikkim Schools;design and Modeling of Modular Wheelchair for therapeutic Exercises;analyzing data Traffic in IoT Networks: A Novel Graph-Based Architecture;A Lightweight privacy-Preserving and Authentication Model (LiPAM-FSG) for Fog-Based Smart Grids;time Series Analysis and Rule Mining for Detecting Industrial Control System data Injection Attacks;Radius-Based Authentication: UFrames’ Novel Approach to Prevent Account Sharing;deep Neural Network-Based Secure Energy-Efficient Power Allocation in an Interference Network;An Adaptive Time Quantum-Based Efficient Round-Robin Algorithm for CPU Scheduling;low-Cost Advanced Undetectable Hardware-Based Malicious Keyboard;constructing a Blind Digital Signature Scheme

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9th International Skin Imaging Collaboration Workshop, ISIC 2024, 7th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2024, Embodied AI and Robotics for Healthcare Workshop, EARth 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

9th International Skin Imaging Collaboration Workshop, ISIC ...

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9th International Skin Imaging Collaboration Workshop, ISIC 2024, 7th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2024, Embodied AI and Robotics for Healthcare Workshop, EARth 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031776090

the proceedings contain 23 papers. the special focus in this conference is on Skin Imaging Collaboration, Interpretability of Machine Intelligence in Medical Image Computing, Embodied AI and Robotics for Healthcare Workshop and MICCAI Workshop on Distributed, Collaborative and Federated Learning. the topics include: DeCaF 2024 Preface;i2M2Net: Inter/Intra-modal Feature Masking Self-distillation for Incomplete Multimodal Skin Lesion Diagnosis;from Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion Analysis;segmentation Style Discovery: application to Skin Lesion Images;a Vision Transformer with Adaptive Cross-Image and Cross-Resolution Attention;lesion Elevation Prediction from Skin Images Improves Diagnosis;DWARF: Disease-Weighted Network for Attention Map Refinement;PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans;Detecting Unforeseen data Properties with Diffusion Autoencoder Embeddings Using Spine MRI data;interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis;TextCAVs: Debugging Vision Models Using Text;evaluating Visual Explanations of Attention Maps for Transformer-Based Medical Imaging;Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI;EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting;VISAGE: Video Synthesis Using Action Graphs for Surgery;a Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery;SurgTrack: CAD-Free 3D Tracking of Real-World Surgical Instruments;MUTUAL: Towards Holistic Sensing and Inference in the Operating Room;Complex-Valued Federated Learning with Differential privacy and MRI applications;enhancing privacy in Federated Learning: Secure Aggregation for Real-World Healthcare applications;federated Impression for Learning with Distributed Heterogeneous data;A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation;probing the Effic

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Distributed data storage auditing scheme in fog computing environment 5

Distributed data storage auditing scheme in fog computing en...

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5th International conference on Computer Communication and Network security, CCNS 2024

作者： Wang, Yi Yang, Lihao Liu, Wang Chen, Dongdong Gu, Ke School of Computer & Communication Engineering Changsha University of Science & Technology Hunan Changsha China Xiangtan Chengfa Technology Group Co. Ltd. Hunan Xiangtan China

ISBN: (纸本)9781510681842

With the continuous innovation of wireless communication technology, fog computing-based distributed storage serves as a useful complement to centralized cloud computing and is trending towards large-scale deployment driven. However, for the distributed storage scenario in fog computing, there is currently a lack of effective data auditing methods. this article proposes a distributed data storage auditing scheme in fog computing environment. In our scheme, we first design a distributed storage model based on fog computing. In this model, we use threshold secret sharing technology to divide the outsourced data into multiple blocks and upload them to multiple fog servers to effectively solve the single point of failure and achieve privacy protection. In addition, we have established a fog device audit consensus group based on the verifiable random function (VRF), which ensures the correctness of audit results through consensus among members. In addition, this article proposes a lightweight validator generation algorithm to address the issue of limited resources at the user end. Finally, experimental results show our distributed data storage auditing scheme is efficient and effective. © 2024 SPIE.

关键词： Fog computing

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A Computer Network Training data Center Resource Monitoring Platform 5

A Computer Network Training Data Center Resource Monitoring ...

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5th International conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Ning, An Chengdu Polytechnic Chengdu China

ISBN: (纸本)9798331527662

this article presents the development of a visual monitoring platform for educational resources, encompassing both hardware components and software applications, specifically designed for the training setting of computer network technology programs in light of the swift advancements in next-generation information technologies. the platform is based on a B/S architecture. It facilitates, supports, and handles operational tasks through three core platforms, including a real-time data platform, an alarm platform, and a monitoring platform. these systems ensure the transparent capture and storage of educational resource data, which subsequently gets uploaded to a cloud management platform to establish a visual operational mechanism. It monitors computer rooms, hardware, and application resources, providing security alarms and maintenance event logs. the system offers real-time data updates and integrates 2D and 3D visualization technologies to display resource information clearly, aiding administrators in managing cloud platform infrastructures. © 2024 IEEE.

关键词： Cloud platforms

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Impact of Cloud Computing in Digital Healthcare 5

Impact of Cloud Computing in Digital Healthcare

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5th IEEE International conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： thakur, Shreya Sharma, Simran Sharma, Arvind K. Kaur, Rajinder Anju Shoolini University Yogananda School of AI Computers and Data Science Himachal Pradesh Solan India Trinity College Department of Computer Sciences Punjab Jalandhar India

ISBN: (纸本)9798350383522

the continuous and sustainable development in healthcare is taking place from decades and with prompt technological advancement in engineering nowadays the healthcare can't be imagined without the advent tools like Cloud Computing, Internet of Medical things etc., for quality medical practices, electronic medical records management, patient control on private health information, tracking/tracing of pharmaceutical shipments etc. the eminent cloud-computing not only spreading its feet but also exploring new dimensions in healthcare to make the digital-health possible with its remarkable fundamental benefits in quality healthcare application development. the paper is focused especially on cloud-computing, its applications and services more specifically in healthcare towards best medical practices. © 2024 IEEE.

关键词： Cloud Computing Digital Health Electronic Health Records Healthcare privacy Telemedicine Wearable Device

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Public-key Authenticated Encryption with Keyword Search: Cryptanalysis, Enhanced security, and Quantum-resistant Instantiation 22

Public-key Authenticated Encryption with Keyword Search: Cry...

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17th acm ASIA conference on Computer and Communications security 2022 (acm ASIACCS)

作者： Liu, Zi-Yuan Tseng, Yi-Fan Tso, Raylin Mambo, Masahiro Chen, Yu-Chi Natl Chengchi Univ Dept Comp Sci Taipei 11605 Taiwan Kanazawa Univ Inst Sci & Engn Kanazawa Ishikawa 9201192 Japan Yuan Ze Univ Dept Comp Sci & Engn Taoyuan 32003 Taiwan Kanazawa Univ Grad Sch Nat Sci & Technol Kanazawa Ishikawa 9201192 Japan Natl Chengchi Univ Artificial Intelligence & Learning Ctr Taipei 11605 Taiwan

ISBN: (纸本)9781450391405

With the rapid development of cloud computing, an increasing number of companies are adopting cloud storage technology to reduce overhead. However, to ensure the privacy of sensitive data, the uploaded data need to be encrypted before being outsourced to the cloud. the concept of public-key encryption with keyword search (PEKS) was introduced by Boneh et al. to provide flexible usage of the encrypted data. Unfortunately, most of the PEKS schemes are not secure against inside keyword guessing attacks (IKGA), so the keyword information of the trapdoor may be leaked to the adversary. To solve this issue, Huang and Li presented public key authenticated encryption with keyword search (PAEKS) in which the trapdoor generated by the receiver is only valid for authenticated ciphertexts. With their seminal work, many PAEKS schemes have been introduced for the enhanced security of PAEKS. Some of them further consider the upcoming quantum attacks. However, our cryptanalysis indicated that in fact, these schemes could not withstand IKGA. To fight against the attacks from quantum adversaries and support the privacy-preserving search functionality, we first introduce a novel generic PAEKS construction in this work. then, we further present the first quantum-resistant PAEKS instantiation based on lattices. the security proofs show that our instantiation not only satisfies the basic requirements but also achieves enhanced security models, namely the multi-ciphertext indistinguishability and multi-trapdoor privacy. Furthermore, the comparative results indicate that with only some additional expenditure, the proposed instantiation provides more secure properties, making it suitable for more diverse application environments.

关键词： Cryptanalysis Generic construction Keyword search Public-key authenticated encryption Post-quantum Trapdoor privacy

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Attack and Defense Methods for Graph Vertical Federation Learning 22

Attack and Defense Methods for Graph Vertical Federation Lea...

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5th International conference on Computer Science and Software Engineering, CSSE 2022

作者： Xie, Xinyi Zheng, Haibin Li, Hu Pang, Ling Chen, Jinyin College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Institute of Cyberspace Security and College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Information System Security Technology Beijing100101 China

ISBN: (纸本)9781450397780

To further protect citizens' privacy and national data security, graph federation learning has been widely used and rapidly developed. However, with the deployment and landing of graph federation learning tasks, the security issues involved are gradually exposed. To deeply study the application security issues of graph federation learning, this paper proposes an attack method and privacy protection defense method for graph data in the framework of vertical federation learning. the research revolves around the attack method. First, noise is randomly generated, combined with the attacker's embedding features, and fed into the server model, and the calculated results are compared with the real values to obtain the loss values. then the attacker's attack model is updated to generate a new inference of the attacked embedding. the experiments conducted on two real-world datasets both obtained MSE metrics below 1, which fully illustrates the effectiveness of the attack method. Further research is conducted around the defense method, which uses a computed differential noise added to the uploaded embedding information to achieve the defense against privacy theft. In the experiments, the related attack metrics are significantly reduced with almost no impact on the main task performance. © 2022 acm.

关键词： Embeddings

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Service Quality Analysis of Telkomsel Case Study Based on Online Customer Reviews in Google Play Store 5

Service Quality Analysis of Telkomsel Case Study Based on On...

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5th IEEE International conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Ariyanti, Maya Widiyanesti, Sri Aprillia, Wahyuning Hanifah Telkom University School of Economics and Business Bandung Indonesia

ISBN: (纸本)9798350383522

A corporation that offers internet services is known as an Internet Service Provider (ISP). Regional-scale networks and worldwide networks are offered at ISPs, allowing consumers to effortlessly connect with the outside world globally. Telkomsel is one of providers that is used the most widely in Indonesia. However, Telkomsel is the most complaints than others. this research chooses Telkomsel as a study case to determine their service quality based on customer review. this research aims to analyzes Telkomsel service quality and topic discussion based on reviews in Google Play Store. We categorized the data according to the following servqual dimension includes network quality, customer service and technical support, information quality, security and privacy, and fulfillment. As a result, the Naive Bayes Classifier (NBC) model was applied for sentiment analysis process. the accuracy for sentiment analysis using NBC was more than 75%. For topic modeling, the research used Latent Dirichlet Allocation (LDA) models to identify popular topics often discussed by customers. Hence, the result of this research can help a firm or organization to improve and increase their quality of product and service according to customer needs. © 2024 IEEE.

关键词： big data sentiment analysis service quality text analytic topic modeling

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Private data Preprocessing for privacy-preserving Federated Learning 5

Private Data Preprocessing for Privacy-preserving Federated ...

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5th IEEE International conference on Knowledge Innovation and Invention, ICKII 2022

作者： Hsu, Ruei-Hau Huang, Ting-Yun National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung Taiwan

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

privacy-preserving federated learning can accomplish model aggregation without leaking to a local model and avoid the problem of sensitive data exposure caused by model leakage. Even though it protects privacy in the training process, any data analysis task proposed by the initiator and the type of data required for the task contains the research or trade secrets of the organization. Being intercepted in the transmission process or known by other data providers, the disclosure of essential research secrets occurs, leading to the theft of research or business ideas. therefore, it is a critical issue to achieve data matching between the initiator and the participant under the premise of privacy protection. In this study, we propose a federated learning framework that considers the above security issues. A privacy-preserving federated learning architecture based on homomorphic encryption is designed to protect each participant's data and local model. In addition, encrypted query technology is used in this architecture to provide data privacy matching. the data provider searches the data in ciphertext, finds the encrypted data that meets the conditions, and completes the training process without disclosing any requirements of the task initiator. © 2022 IEEE.

关键词： Sensitive data

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Design thinking Approach for Dimensionality Reduction using Ensemble Gradient Boosting Algorithm 5

Design Thinking Approach for Dimensionality Reduction using ...

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5th International conference on Inventive Research in Computing applications, ICIRCA 2023

作者： Selva Kumar, N. Kalaivani, D. Dr.SNS Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore India Dr.SNS Rajalakshmi College of Arts and Science Department of Computer Technology Coimbatore India

ISBN: (纸本)9798350321425

Human activity recognition (HAR) has developed an essential application in the Internet of things (IoT) domain, providing a foundation for many healthcare, fitness, and security applications. However, IoT sensors' high volume of data generation can make HAR computationally challenging. Dimensionality reduction techniques have been proposed to address this challenge, however, their performance is dependent on a number of criteria, like data quality, algorithm selection, and feature space design. this paper propose a design thinking approach for dimensionality reduction in enhancing HAR in IoT using data analytics and machine learning algorithms. this research work present a comprehensive framework that incorporates user-centric design, data exploration, feature engineering, and machine learning modeling to develop the accuracy and efficiency of HAR systems. the performance of this method is assessed using a publicly available dataset for HAR and results show that our framework achieves a significant reduction in feature space with minimal loss of information, leading to improved accuracy value and efficiency of HAR systems. this research explores into the application of design thinking in addressing dimensionality reduction difficulties in HAR, which can have important implications for the development of efficient and user-friendly IoT applications. the proposed approach aims to create more efficient and user-friendly IoT applications in areas such as healthcare, fitness, and security. © 2023 IEEE.

关键词： data Analytics

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