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An Incentive Mechanism for Privacy Preserved Data Trading with Verifiable Data Disturbance

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Man Li, Xinghua Luo, Bin Ren, Yanbing Miao, Yinbin Liu, Ximeng Deng, Robert H. State Key Laboratory of Integrated Services Networks the School of Cyber Engineering Xidian University Xi’an710071 China Engineering Research Center of Big data Security Ministry of Education Xi’an710071 China The College of Mathematics and Computer Science Fuzhou University Fujian350108 China The Department of Information Systems Singapore Management University 80 Stamford Road Singapore

To motivate data owners’ (DOs’) trading willingness, the existing incentive mechanisms allow DOs to independently disturb data following data consumer’s (DC’s) availability requirement. However, they cannot motivate DOs’ honest disturbance, which is attributed to DOs’ independent disturbance without any supervision. Thus, we implement an incentive mechanism for privacy preserved data trading with verifiable data disturbance where an honest-but-curious disturbance generator (DG) is additionally introduced to supervise DOs’ local disturbance and assist disturbance verification between DOs and DC. Specifically, DG generates the disturbance strategies and secretly distributes to DOs following private information retrieval, guaranteeing DOs’s local disturbance’s privacy and verifiability with our proposed three-level verification algorithm. Subsequently, we model the trading as a game and disturbance verification results determine the compensation and punishment for trading bilateral utilities following Nash Equilibrium where DOs honestly disturb data. Theoretical analysis shows that DOs are motivated to honestly disturb data and their raw data privacy is preserved. Extensive experiments using the real-world dataset demonstrate that the deviating DOs in our scheme can be verified with a probability of more than 90% and the statistical result accuracy can be improved by more than 80% compared with the existing works. © 2004-2012 IEEE. All rights reserved.

关键词： Differential privacy

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Utilizing low-cost optical sensor for the measurement of particulate matter and calculating Pearson's correlation coefficient 16

Utilizing low-cost optical sensor for the measurement of par...

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16th International Conference on Correlation Optics, COR 2023

作者： Kirešová, Simona Rusyn, Volodymyr Guzan, Milan Vorobets, George Sobota, Branislav Vorobets, Oleksandr Dept. of Theoretical and Industrial Electrical Engineering Technical University of Košice Letná 9 Košice042 00 Slovakia Dept. of Radio Engineering and Information Security Yuriy Fedkovych Chernivtsi National University 2 Kotsjubynskyi Str. Chernivtsi58000 Ukraine Dept. of Computer Systems and Networks Yuriy Fedkovych Chernivtsi National University 2 Kotsjubynskyi Str. Chernivtsi58000 Ukraine Dept. of Computers and Informatics Technical University of Košice Letná 9 Košice042 00 Slovakia

ISBN: (纸本)9781510671829

This article discusses the possibilities of measuring Particulate Matter using optical low-cost sensors. Depending on the sensor used, not only can there be problems with the positioning of the sensor with respect to the incoming sunlight, but it may also be possible to measure ultrafine particles. While PM2.5 and PM10 are commonly used to characterize air quality, in this paper we point out the need to pay more attention to smaller particles up to 0.1 μm in size (also referred to as ultrafine particulates) due to their highly negative effects on the human health. Attention is also paid to the correlation between particulate matter and meteorological factors like pressure, temperature, and humidity. However, the sudden change in Pearson's correlation coefficient unveiled a need to look into the effect of the wind on particulate matter. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Optical sensors

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Overview Of Vanet Network security

Overview Of Vanet Network Security

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2022 International Conference on Information Science and Communications Technologies, ICISCT 2022

作者： Khujamatov, Halimjon Lazarev, Amir Akhmedov, Nurshod Asenbaev, Nurbek Bekturdiev, Aybek Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Faculty 'Computer Engineering' Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Data Communication Networks and Systems Department Tashkent Uzbekistan Nukus Branch of the Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Information Security Department Nukus Uzbekistan

ISBN: (纸本)9781665472296

This article provides an overview of the security of VANET, which is a vehicle network. When reviewing this topic, publications of various researchers were considered. The article provides information security requirements for VANET, an overview of security research, an overview of existing attacks, methods for detecting attacks and appropriate countermeasures against such threats. © 2022 IEEE.

关键词： Network security

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AI in Education: Improving Quality for Both Centralized and Decentralized Frameworks

AI in Education: Improving Quality for Both Centralized and ...

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IEEE Education Engineering (EDUCON)

作者： Nisha Thorakkattu Madathil Saed Alrabaee Mousa Al-kfairy Rafat Damseh Abdelkader N Belkacem Department of Information Systems and Security College of IT United Arab Emirates University UAE College of Technological Innovation Zayed University Dubai United Arab Emirates Department of Computer Science and Software Engineering College of IT United Arab Emirates University UAE Department of Computer Engineering and Networks College of IT United Arab Emirates University UAE

Education is essential for achieving many Sustainable Development Goals (SDGs). Therefore, the education system focuses on empowering more educated people and improving the quality of the education system. One of the latest technologies to enhance the quality of education is Artificial Intelligence (AI)-based Machine Learning (ML). As a result, ML has a significant influence on the education system. ML is currently widely applied in the education system for various tasks, such as creating models by monitoring student performance and activities that accurately predict student outcomes, their engagement in learning activities, decision-making, problem-solving capabilities, etc. In this research, we provide a survey of machine learning frameworks for both distributed (clusters of schools and universities) and centralized (university or school) educational institutions to predict the quality of students' learning outcomes and find solutions to improve the quality of their education system. Additionally, this work explores the application of ML in teaching and learning for further improvements in the learning environment for centralized and distributed education systems.

关键词： Surveys Learning systems Federated learning Education Predictive models Problem-solving Task analysis

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VFGCN: A Vertical Federated Learning Framework With Privacy Preserving for Graph Convolutional Network

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Gang Li, Qingming Liu, Ximeng Yan, Xiaoran Dong, Qingkuan Wu, Huiwen Kong, Xiangjie Zhou, Li Zhejiang Lab HangZhou310000 China Zhejiang University College of Computer Science and Technology Hangzhou310058 China Fuzhou University Key Laboratory of Information Security of Network Systems College of Computer and Big Data Fuzhou350108 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Dalian University of Technology School of Software Dalian116620 China

Due to the robust representational capabilities of graph data, employing graph neural networks for its processing has demonstrated superior performance over conventional deep learning algorithms. Graph data encompasses abundant features and structural information;however, its large-scale collection is often challenging in practice. This difficulty arises because data predominantly exists in isolated compartments, making it arduous to harmonize information across various organizations or to enable multiple organizations to collaborate effectively while safeguarding local data privacy. In light of an extreme data distribution scenario, where each client possesses distinct nodes with partially overlapping segments yet divergent data features, we introduce a dual-cloud server architecture. This framework encompasses the design of four secure subprotocols: ReEnc (secure re-encryption), SecPSI (secure outsourcing of PSI), SecWeight (secure weight calculation), and SecAgg (secure aggregation). Together, these components facilitate a vertical federated learning framework for graph convolutional networks, ensuring privacy preservation. We provide a security proof for the entire system and extensive evaluation on three benchmark datasets (Cora, Citeseer, and Pubmed) illustrates that our Vertical Federated Graph Convolutional Network (VFGCN) surpasses existing privacy-preserving methodologies. © 2004-2012 IEEE.

关键词： Privacy by design

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AI-Driven Intelligent IoT systems for Real-Time Food Quality Monitoring and Analysis

AI-Driven Intelligent IoT Systems for Real-Time Food Quality...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Prabhakar Krishnan U. Samson Ebenezar R. Ranitha N. Purushotham T. Suresh Balakrishnan Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu Department of Computer Science and Engineering Mohan Babu University AP India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The system will monitor and analyse food quality along the supply chain in real time. The system has high performance due to the CNN algorithms. In terms of accuracy, CNN has a high performance (95.2%), precision (92.8%), recall (94.5%) and F1 score (93.6 %). These features enable the model to predict food quality under good and poor conditions, thus demonstrating its effectiveness in assessing quality. The accuracy of real-time monitoring was evaluated across the supply chain to prove system reliability. Accuracy ratings for transport, storage and processing were 96.3%, 94.7% and 92.1%. These results show the robustness of this system within dynamic supply chain environments. Further testing evaluated environmental resilience capabilities. Under normal conditions, the accuracy of this system was 95.2%, while it reduced to 92.6% and rises again up to a level of 94.3%. The adaptability of the system under harsh conditions demonstrates that it can be applied to ensure food quality in various cases. This study reveals how proposed AI-based intelligent Internet of Things system may be used for real application using an appropriate dataset selected properly. This approach can help improve the quality assurance of food in an industry that is complex by uniting advanced algorithms with real-time data monitoring.

关键词： Temperature sensors Heuristic algorithms Supply chains Transportation Prediction algorithms Real-time systems Internet of Things

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SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation

arXiv

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arXiv 2024年

作者： Xu, Rongtao Wang, Changwei Zhang, Jiguang Xu, Shibiao Meng, Weiliang Zhang, Xiaopeng The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China CASIA Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence Beijing University of Posts and Telecommunications China

Accurate skin lesion segmentation from dermoscopic images is of great importance for skin cancer diagnosis. However, automatic segmentation of melanoma remains a challenging task because it is difficult to incorporate useful texture representations into the learning process. Texture representations are not only related to the local structural information learned by CNN, but also include the global statistical texture information of the input image. In this paper, we propose a transFormer network (SkinFormer) that efficiently extracts and fuses statistical texture representation for Skin lesion segmentation. Specifically, to quantify the statistical texture of input features, a Kurtosis-guided Statistical Counting Operator is designed. We propose Statistical Texture Fusion Transformer and Statistical Texture Enhance Transformer with the help of Kurtosis-guided Statistical Counting Operator by utilizing the transformer’s global attention mechanism. The former fuses structural texture information and statistical texture information, and the latter enhances the statistical texture of multi-scale features. Extensive experiments on three publicly available skin lesion datasets validate that our SkinFormer outperforms other SOAT methods, and our method achieves 93.2% Dice score on ISIC 2018. It can be easy to extend SkinFormer to segment 3D images in the future. Our code is available at https://***/RongtaoXu/SkinFormer. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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SHaRPose: Sparse High-Resolution Representation for Human Pose Estimation

arXiv

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arXiv 2023年

作者： An, Xiaoqi Zhao, Lin Gong, Chen Wang, Nannan Wang, Di Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Integrated Services Networks Xidian University China

High-resolution representation is essential for achieving good performance in human pose estimation models. To obtain such features, existing works utilize high-resolution input images or fine-grained image tokens. However, this dense high-resolution representation brings a significant computational burden. In this paper, we address the following question: "Only sparse human keypoint locations are detected for human pose estimation, is it really necessary to describe the whole image in a dense, high-resolution manner?" Based on dynamic transformer models, we propose a framework that only uses Sparse High-resolution Representations for human Pose estimation (SHaRPose). In detail, SHaRPose consists of two stages. At the coarse stage, the relations between image regions and keypoints are dynamically mined while a coarse estimation is generated. Then, a quality predictor is applied to decide whether the coarse estimation results should be refined. At the fine stage, SHaRPose builds sparse high-resolution representations only on the regions related to the keypoints and provides refined high-precision human pose estimations. Extensive experiments demonstrate the outstanding performance of the proposed method. Specifically, compared to the state-of-the-art method ViTPose, our model SHaRPose-Base achieves 77.4 AP (+0.5 AP) on the COCO validation set and 76.7 AP (+0.5 AP) on the COCO test-dev set, and infers at a speed of 1.4× faster than ViTPose-Base. Code is available at https://***/AnxQ/sharpose. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Arabic Documents Clustering using Bond Energy Algorithm and Genetic Algorithm

Arabic Documents Clustering using Bond Energy Algorithm and ...

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International Conference on Information and Communication systems (ICICS)

作者： Hadeel Alazzam Orieb AbuAlghanam Abdulsalam Alsmady Esra’ a Alhenawi Department of Intelligence Systems Al-Balqa Applied University Al-Salt Jordan Networks and Information Security Alahliyya Amman University Amman Jordan Department of Computer Engineering Jordan University of Science and Technology Irbid Jordan Department of Computer Science University of Jordan Amman Jordan

Text clustering is a popular data mining process used in data indexing and information retrieval. However, Existing clustering techniques suffer from several shortcomings such as sensitivity to the initial value, slow convergence, etc... In this paper, the Bond Energy Algorithm (BEA) has been used for clustering Arabic documents. The bond energy algorithm has been modified with the help of a Genetic algorithm to enhance the accuracy of the results. The efficiency of modified BEA has been compared to BEA baseline and K-means clustering in terms of precision, recall, and F-Score. The results show that Modified BEA can be used for clustering Arabic Documents, and the efficiency of the algorithm can be enhanced in the future to give a better result.

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Applied Aspects of Polyharmonic Functions for security and Reliability of Data Transmission

Applied Aspects of Polyharmonic Functions for Security and R...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Oleksandr Laptiev Andrii Sobchuk Anton Ryzhov Oleh Perehuda Iryna Tsygansvska Serhii Kopytko Department of Cyber Security and Information Protection Faculty of information technology Taras Shevchenko National University of Kyiv Kyiv Ukraine Department of Information and Cyber Security State University of Information and Communication Technologies Kyiv Ukraine Department of General Mathematics Faculty of Mechanics and Mathematics Taras Shevchenko National University of Kyiv Kyiv Ukraine Department of Algebra and Computer Mathematics Faculty of Mechanics and Mathematics Taras Shevchenko National University of Kyiv Kyiv Ukraine Department of Information Systems and Networks Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798350354423

ISBN: (纸本)9798350354430

At present, when armed conflicts have intensified, and especially against the background of Russia's armed aggression against Ukraine, a new era of development of high-precision means of protection and weapons has begun. Their effectiveness is primarily determined by the state of radio-electronic technical means. These include means of communication and control, radar and hydroacoustics, telemetry, etc. Thus, the effectiveness of the solution of the tasks before the radio-electronic device depends on the class of signals used. In particular, the type of signal depends on the quality of the communication, the range, the resolution capabilities according to various parameters, the probability of detection, the quality of control, stealth, etc. But the detection of hidden signals requires high-quality signal processing with consistent mathematical analysis of the signal. This requires a powerful mathematical apparatus. As an example of signal processing, the article discusses methods of using signal transformation using polyharmonic operators. The transformation of signals by two types of operators is presented: the Abel-Poisson operator and the Weierstrass operator. The simulation results proved the advantages of Weierstrass operators for signal transformation.

关键词： Weapons Simulation Mathematical analysis Radar Telecommunications Telemetry Telecommunication network reliability security Protection Signal resolution

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