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Cheap and secure metatransactions on the blockchain using hash-based authorisation and preferred batchers

Blockchain(Research and Applications) 2023年第2期4卷 41-66页

作者： William Hughes Tobias Magnusson Alejandro Russo Gerardo Schneider Dept.of Computer Science and Engineering University of Gothenburg41296GothenburgSweden Dept.of Computer Science and Engineering Chalmers University41296GothenburgSweden

Smart contracts are self-executing programs running in the blockchain allowing for decentralised storage and execution without a ***-chain execution is expensive,with miners charging fees for distributed execution according to a cost model defined in the *** particular,transactions have a high fixed *** present MultiCall,a transaction-batching interpreter for Ethereum that reduces the cost of smart contract executions by gathering multiple users’transactions into a *** current implementation of MultiCall includes the following features:the ability to emulate Ethereum calls and create transactions,both from MultiCall itself and using an identity unique to the user;the ability to cheaply pay Ether to other MultiCall users;and the ability to authorise emulated transactions on behalf of multiple users in a single transaction using hash-based authorisation rather than more expensive *** improves upon a previous version of *** experiments show that MultiCall provides a saving between 57%and 99%of the fixed transaction cost compared with the standard approach of sending Ethereum transactions directly.

关键词： Ethereum Domain-specific language Interpreter Gas optimisation

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A secure double spectrum auction scheme

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Digital Communications and Networks 2024年第5期10卷 1415-1427页

作者： Jiaqi Wang Ning Lu Ziyang Gong Wenbo Shi Chang Choi School of Computer Science and Engineering Northeastern UniversityShenyang110004China School of Computer Science and Technology Xidian UniversityXi©anChina Dept.of Computer Engineering Gachon University1342Seongnam-daeroSujeong-guSeongnam-si13120G School of Computer and Communication Engineering Northeastern UniversityQinhuangdao066004China

With the arrival of the 5G era,wireless communication technologies and services are rapidly exhausting the limited spectrum *** auctions came into being,which can effectively utilize spectrum *** of the complexity of the electronic spectrum auction network environment,the security of spectrum auction can not be *** scholars focus on researching the security of the single-sided auctions,while ignoring the practical scenario of a secure double spectrum auction where participants are composed of multiple sellers and *** begin to design the secure double spectrum auction mechanisms,in which two semi-honest agents are introduced to finish the spectrum auction *** these two agents may collude with each other or be bribed by buyers and sellers,which may create security risks,therefore,a secure double spectrum auction is proposed in this *** traditional secure double spectrum auctions,the spectrum auction server with Software Guard Extensions(SGX)component is used in this paper,which is an Ethereum blockchain platform that performs spectrum auctions.A secure double spectrum protocol is also designed,using SGX technology and cryptographic tools such as Paillier cryptosystem,stealth address technology and one-time ring signatures to well protect the private information of spectrum *** addition,the smart contracts provided by the Ethereum blockchain platform are executed to assist offline verification,and to verify important spectrum auction information to ensure the fairness and impartiality of spectrum ***,security analysis and performance evaluation of our protocol are discussed.

关键词： Secure double spectrum auction SGX technology Privacy information Ethereum platform Verification

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3-Multi ranked encryption with enhanced security in cloud computing

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Digital Communications and Networks 2023年第2期9卷 313-326页

作者： YeEun Kim Junggab Son Reza M.Parizi Gautam Srivastava Heekuck Oh Major in Bio Artificial Intelligence Dept.of Computer Science and EngineeringHanyang UniversityAnsanRepublic of Korea Department of Computer Science Kennesaw State UniversityMariettaUSA Department of Software Engineering Kennesaw State UniversityMariettaUSA Department of Mathematics and Computer Science Brandon UniversityBrandonCanada Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Dept.of Computer Science and Engineering Hanyang UniversityAnsanRepublic of Korea

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when *** refer to a model that satisfies all of the conditions a 3-multi ranked search ***,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation *** is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real *** this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security *** proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same ***,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different *** evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

关键词： Searchable encryption Multi-keyword search Multiple data owners/users Ranked search Enhanced security

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RFID-based 3D human pose tracking: A subject generalization approach

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Digital Communications and Networks 2022年第3期8卷 278-288页

作者： Chao Yang Xuyu Wang Shiwen Mao Dept.of Electrical and Computer Engineering Auburn UniversityAuburnAL36849-5201USA Dept.of Computer Science California State UniversitySacramentoCA95819-6021USA

Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.

关键词： Radio-frequency identification(RFID) Three-dimensional(3D)human pose tracking Cycle-consistent adversarial network Generalization

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Energy Efficient LEDCA Analysis for Improved Throughput in Wireless Sensor Networks 6

Energy Efficient LEDCA Analysis for Improved Throughput in W...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Padmageetha, B.G. Naik, Pramod Kumar Patil, Mallanagouda Dept.of Computer Applications Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering RVITM Bengaluru India

ISBN: (纸本)9798331522667

Wireless Sensor Networks (WSNs) face significant challenges in terms of energy conservation, scalability, and data throughput owing to their limited node energy resources. This paper presents a low-energy data-centric algorithm (LEDCA), a novel energy-aware clustering and routing protocol designed to optimize energy efficiency and improve throughput in WSNs. Through simulations, LEDCA demonstrated a Packet Delivery Ratio (PDR) of up to 90%, throughput of 255,217 bps, and average latency of 159 ms, validating its efficiency in resource-constrained environments. The algorithm integrates residual energy awareness by prioritizing high-energy nodes for Cluster Head (CH) roles, dynamically adjusting routing paths, and balancing the network load to prevent premature node failure. By applying an Energy Mapping Node and a real-time Energy Mapping Table, LEDCA minimizes re-clustering, reduces data redundancy, and ensures stable data transmission even under high-density networks. Simulation results also show LEDCA's scalability of LEDCA, with A9_T_200 achieving the highest throughput under extended transmission ranges and A3_T_200 achieving the lowest latency, making it suitable for energy-sensitive and latency-critical applications. Future enhancements, such as integrating Non-Orthogonal Multiple Access (NOMA) and dynamic resource allocation, could further improve LEDCA’s interference management and scalability of LEDCA for IoT and 5G/6G networks, addressing the needs of next-generation applications. © 2025 IEEE.

关键词： Resource allocation

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ZEROTH-ORDER OPTIMIZATION WITH TRAJECTORY-INFORMED DERIVATIVE ESTIMATION 11

ZEROTH-ORDER OPTIMIZATION WITH TRAJECTORY-INFORMED DERIVATIV...

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11th International Conference on Learning Representations, ICLR 2023

作者： Shu, Yao Dai, Zhongxiang Sng, Weicong Verma, Arun Jaillet, Patrick Low, Bryan Kian Hsiang Dept.of Computer Science National University of Singapore Singapore Dept.of Electrical Engineering and Computer Science MIT United States

Zeroth-order (ZO) optimization, in which the derivative is unavailable, has recently succeeded in many important machine learning *** algorithms rely on finite difference (FD) methods for derivative estimation and gradient descent (GD)-based approaches for ***, these algorithms suffer from query inefficiency because many additional function queries are required for derivative estimation in their every GD update, which typically hinders their deployment in real-world applications where every function query is *** this end, we propose a trajectory-informed derivative estimation method which only employs the optimization trajectory (i.e., the history of function queries during optimization) and hence can eliminate the need for additional function queries to estimate a ***, based on our derivative estimation, we propose the technique of dynamic virtual updates, which allows us to reliably perform multiple steps of GD updates without reapplying derivative *** on these two contributions, we introduce the zeroth-order optimization with trajectory-informed derivative estimation (ZORD) algorithm for query-efficient ZO *** theoretically demonstrate that our trajectory-informed derivative estimation and our ZORD algorithm improve over existing approaches, which is then supported by our real-world experiments such as black-box adversarial attack, non-differentiable metric optimization, and derivative-free reinforcement learning. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Trajectories

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Non-Invasive Oral Cancer Detection Using Hyperspectral Imaging and Advanced Spectral Unmixing Models

Non-Invasive Oral Cancer Detection Using Hyperspectral Imagi...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Ayyapa, Valluri Kothamasu, Kesava Sriram Killaru, Priyusha Muddana, Saadhivik Aala, Suresh Gutha, Vamsi Kumar, Mukkara Prasana Chinnadurai, Sunil SRM University Dept.of Electronics and Communication Engineering AP India SRM University Dept. of Computer Science and Engineering AP India SRM University Dept. of Computer Science AP India

ISBN: (纸本)9798331508432

Oral cancer is a significant global health concern, often leading to high mortality rates due to late-stage diagnosis and the lack of effective early detection methods. Despite advances in medical science, the absence of reliable early diagnostic tools remains a critical challenge. Hyperspectral imaging (HSI) has emerged as a powerful noninvasive technology, capturing detailed spectral information across a wide range of wavelengths. This allows for accurate differentiation between cancerous and healthy tissues, improving early detection and enhancing treatment outcomes. In this study, we propose the use of HSI for early oral cancer diagnosis. To address the scarcity of labeled data, we developed a synthetic hyperspectral dataset that includes spectral signatures of both normal and cancerous tissues. The dataset was generated using a bilinear mixing model, with key spectral features extracted through Vertex Component Analysis (VCA) and abundances computed using Non-Negative Least Squares (NNLS). The model's performance was evaluated using Spectral angle distance (SAD) and Root mean square error (RMSE) metrics. Our findings demonstrate that HSI significantly improves the accuracy of early oral cancer detection, outperforming traditional methods. This work highlights the potential of advanced imaging technologies in revolutionizing cancer diagnosis, offering a robust framework for non-invasive detection and showcasing the effectiveness of synthetic datasets in medical imaging research. © 2024 IEEE.

关键词： Deep Learning Hyperspectral Imaging (HSI) Non-Negative Least Squares (NNLS) Oral Cancer Detection Root Mean Square Error (RMSE) Spectral Angle Distance (SAD) Synthetic Dataset Vertex Component Analysis (VCA)

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A Multi-Model Rouge Nodes Detection System for Fog Computing

A Multi-Model Rouge Nodes Detection System for Fog Computing

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2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Bui, Thanh Duc Panda, Brajendra University of Arkansas Dept. of Computer Science and Computer Engineering Fayetteville United States

ISBN: (纸本)9798350327595

With the increasing speed of advance of both Internet of Things (IoT) and the demands of utilizing IoT devices in different locations with high compatibility, fog computing has been foreseen as a sustainable solution. However, new challenges related to fog computing's cyber security also emerge. Among various cyber-attacks on the fog computing system, one standard method is to implement corrupted internal nodes that allow malicious/compromised access from attackers, threatening the confidentiality of users' information and the system's performance. In this paper, we propose a method to quantify the distribution of each attribute value from historical access logs from each node within a fog computing system. We also utilize an unsupervised machine learning method to separate nodes into smaller clusters with respect to different models to improve the forecasting ability of the system. Finally, our models are evaluated over simulated data using real access logs. Our experimental results illustrate that our proposed method achieves some advanced performance over applying an overall classification, even using the same classification machine learning methods and dataset. © 2023 IEEE.

关键词： Cloud Computing Cyber Security Data Mining Machine Learning

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Information theory based clustering of cellular network usage data for the identification of representative urban areas

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Digital Communications and Networks 2024年第6期10卷 1677-1685页

作者： Mihaela I.Chidean Luis Ignacio Jiménez Gil Javier Carmona-Murillo David Cortés-Polo Dept.of Signal Theory and Communication Univ.Rey Juan CarlosFuenlabrada 28942Spain Computer Science University of ValladolidP.de Belén15Valladolid47011Spain Dept.of Computing and Telematics Engineering Univ.de ExtremaduraMérida06800Spain Dept.of Computing and Telematics Engineering Univ.de ExtremaduraCÃceres 10003Spain

The exponential growth of the number of network devices in recent years not only entails the need for automation of management tasks,but also leads to the increase of available network data and metadata.5G and beyond standards already cover those requirements and also include the need to define and use machine learning techniques to take advantage of the data acquired,especially using geolocated Call Detail Record(CDR)data ***,this scenario requires novel cellular network analysis methodologies to exploit all these available data,especially for the network usage pattern in order to ease the management *** this work,a novel method based on information theory metrics like the Kullback-Leibler divergence and data classification algorithms is proposed to identify representative urban areas in terms of the network usage *** validation is performed via computer analysis using the Open Big Data CDR data set in the Milan area for different *** results validate the proposed methodology and also reveal its adaptability in terms of specific scenario *** usage patterns are calculated for each representative area,paving the path to several future research lines in network management,such as network usage prediction based on this methodology and using the comportment time series.

关键词： Clustering 5G Network analysis Multi-feature analysis Kullback-Leibler divergence Cellular networks

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Towards Comprehensive Breast Cancer Detection: Fusion of Dense Layer Model Features 15

Towards Comprehensive Breast Cancer Detection: Fusion of Den...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, Gadawalsa Kishor Muduli, Debendra Rath, Kshirabdi Tanaya Ray, Durbar Sundar Sharma, Santosh Kumar C.V Raman Global University Computer Science dept.of Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350370249

For an early diagnosis and successful treatment, ultrasonography image analysis is essential for identifying breast cancer. In this discipline, deep learning has become a powerful tool that has demonstrated notable success in several medical imaging tasks,regarding the identification of cancer in the breast. In order to achieve accurate identification, our suggested model combines characteristics from three pre-trained Convolutional Neural Network (CNN) models: DenseNet201, DenseNet169, and DenseNet121. In this study, we use ImageNet weights and ultrasound image datasets to evaluate these models' performance. After extensive testing, our model reached 94.57% accuracy, which is astounding. The model's performance was evaluated through the use of confusion matrices and metrics such as precision & F1-score, which we determined using the Adam optimizer. The efficacy of deep learning-based techniques in identifying breast cancer from ultrasound pictures is highlighted by this study. It illustrates makes major contributions to medical analysis and helps discover breast cancers early. © 2024 IEEE.

关键词： Breast Cancer Deep learning DenseNet121 DenseNet169 DenseNet201 Machine learning Medical imaging ultrasound imaging

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