The data asset is emerging as a crucial component in both industrial and commercial applications. Mining valuable knowledge from the data benefits decision-making and business. However, the usage of data assets raises tension between sensitive information protection and value estimation. As an emerging machine learning paradigm, Federated Learning (FL) allows multiple clients to jointly train a global model based on their data without revealing it. This approach harnesses the power of multiple data assets while ensuring their privacy. Despite the benefits, it relies on a central server to manage the training process and lacks quantification of the quality of data assets, which raises privacy and fairness concerns. In this work, we present a novel framework that combines Federated Learning and Blockchain by Shapley value (FLBS) to achieve a good trade-off between privacy and fairness. Specifically, we introduce blockchain in each training round to elect aggregation and evaluation nodes for training, enabling decentralization and contribution-aware incentive distribution, with these nodes functionally separated and able to supervise each other. The experimental results validate the effectiveness of FLBS in estimating contribution even in the presence of heterogeneity and noisy data.
数据资产正在成为工业和商业应用中的重要组成部分。从数据中挖掘有价值的知识有利于决策和业务。然而，数据资产的使用引发了敏感信息保护与价值评估之间的矛盾。作为一种新兴的机器学习范式，联合学习（FL）允许多个客户在不泄露数据的情况下，基于其数据共同训练一个全局模型。这种方法既能利用多种数据资产的力量，又能确保其隐私。尽管好处多多，但它依赖于一个中央服务器来管理训练过程，缺乏对数据资产质量的量化，从而引发了对隐私和公平性的担忧。在这项工作中，我们提出了一个新颖的框架，将联邦学习（Federated Learning）和夏普利区块链（Blockchain by Shapley value，FLBS）结合起来，在隐私和公平性之间实现了良好的权衡。具体来说，我们在每一轮训练中引入区块链，选举出训练的聚合节点和评估节点，实现去中心化和贡献感知的激励分配，这些节点在功能上相互分离并能够相互监督。实验结果验证了 FLBS 在估计贡献方面的有效性，即使在存在异质性和噪声数据的情况下也是如此。
{"title":"Quantifying Bytes: Understanding Practical Value of Data Assets in Federated Learning","authors":"Minghao Yao;Saiyu Qi;Zhen Tian;Qian Li;Yong Han;Haihong Li;Yong Qi","doi":"10.26599/TST.2024.9010034","DOIUrl":"https://doi.org/10.26599/TST.2024.9010034","url":null,"abstract":"The data asset is emerging as a crucial component in both industrial and commercial applications. Mining valuable knowledge from the data benefits decision-making and business. However, the usage of data assets raises tension between sensitive information protection and value estimation. As an emerging machine learning paradigm, Federated Learning (FL) allows multiple clients to jointly train a global model based on their data without revealing it. This approach harnesses the power of multiple data assets while ensuring their privacy. Despite the benefits, it relies on a central server to manage the training process and lacks quantification of the quality of data assets, which raises privacy and fairness concerns. In this work, we present a novel framework that combines Federated Learning and Blockchain by Shapley value (FLBS) to achieve a good trade-off between privacy and fairness. Specifically, we introduce blockchain in each training round to elect aggregation and evaluation nodes for training, enabling decentralization and contribution-aware incentive distribution, with these nodes functionally separated and able to supervise each other. The experimental results validate the effectiveness of FLBS in estimating contribution even in the presence of heterogeneity and noisy data.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"135-147"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.26599/TST.2023.9010145
Beiteng Yang;Haibin Zhu;Dongning Liu
The Vehicle Allocation Problem (VAP) in the vehicle sales chain has three bottlenecks in practice. The first is to collect relevant cooperation or conflict information, the second is to accurately quantify and analyze other factors affecting the distribution of cars, and the third is to establish a stable and rapid response to the vehicle allocation management method. In order to improve the real-time performance and reliability of vehicle allocation in the vehicle sales chain, it is crucial to find a method that can respond quickly and stabilize the vehicle allocation strategy. Therefore, this paper addresses these issues by extending Group Multi-Role Assignment with Cooperation and Conflict Factors (GMRACCF) from a new perspective. Through the logical reasoning of closure computation, the KD45 logic algorithm is used to find the implicit cognitive Cooperation and Conflict Factors (CCF). Therefore, a collaborative filtering comprehensive evaluation method is proposed to help administrators determine the influence weight of CCFs and Cooperation Scales (CSs) on the all-round performance according to their needs. Based on collaborative filtering, semantic modification is applied to resolve conflicts among qualifications. Large-scale simulation results show that the proposed method is feasible and robust, and provides a reliable decision-making reference in the vehicle sales chain.
{"title":"Improve GMRACCF Qualifications via Collaborative Filtering in Vehicle Sales Chain","authors":"Beiteng Yang;Haibin Zhu;Dongning Liu","doi":"10.26599/TST.2023.9010145","DOIUrl":"https://doi.org/10.26599/TST.2023.9010145","url":null,"abstract":"The Vehicle Allocation Problem (VAP) in the vehicle sales chain has three bottlenecks in practice. The first is to collect relevant cooperation or conflict information, the second is to accurately quantify and analyze other factors affecting the distribution of cars, and the third is to establish a stable and rapid response to the vehicle allocation management method. In order to improve the real-time performance and reliability of vehicle allocation in the vehicle sales chain, it is crucial to find a method that can respond quickly and stabilize the vehicle allocation strategy. Therefore, this paper addresses these issues by extending Group Multi-Role Assignment with Cooperation and Conflict Factors (GMRACCF) from a new perspective. Through the logical reasoning of closure computation, the KD45 logic algorithm is used to find the implicit cognitive Cooperation and Conflict Factors (CCF). Therefore, a collaborative filtering comprehensive evaluation method is proposed to help administrators determine the influence weight of CCFs and Cooperation Scales (CSs) on the all-round performance according to their needs. Based on collaborative filtering, semantic modification is applied to resolve conflicts among qualifications. Large-scale simulation results show that the proposed method is feasible and robust, and provides a reliable decision-making reference in the vehicle sales chain.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"247-261"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heterogeneous graphs contain multiple types of entities and relations, which are capable of modeling complex interactions. Embedding on heterogeneous graphs has become an essential tool for analyzing and understanding such graphs. Although these meticulously designed methods make progress, they are limited by model design and computational resources, making it difficult to scale to large-scale heterogeneous graph data and hindering the application and promotion of these methods. In this paper, we propose Restage, a relation structure-aware hierarchical heterogeneous graph embedding framework. Under this framework, embedding only a smaller-scale graph with existing graph representation learning methods is sufficient to obtain node representations on the original heterogeneous graph. We consider two types of relation structures in heterogeneous graphs: interaction relations and affiliation relations. Firstly, we design a relation structure-aware coarsening method to successively coarsen the original graph to the top-level layer, resulting in a smaller-scale graph. Secondly, we allow any unsupervised representation learning methods to obtain node embeddings on the top-level graph. Finally, we design a relation structure-aware refinement method to successively refine the node embeddings from the top-level graph back to the original graph, obtaining node embeddings on the original graph. Experimental results on three public heterogeneous graph datasets demonstrate the enhanced scalability of representation learning methods by the proposed Restage. On another large-scale graph, the speed of existing representation learning methods is increased by up to eighteen times at most.
{"title":"Restage: Relation Structure-Aware Hierarchical Heterogeneous Graph Embedding","authors":"Huanjing Zhao;Pinde Rui;Jie Chen;Shu Zhao;Yanping Zhang","doi":"10.26599/TST.2023.9010147","DOIUrl":"https://doi.org/10.26599/TST.2023.9010147","url":null,"abstract":"Heterogeneous graphs contain multiple types of entities and relations, which are capable of modeling complex interactions. Embedding on heterogeneous graphs has become an essential tool for analyzing and understanding such graphs. Although these meticulously designed methods make progress, they are limited by model design and computational resources, making it difficult to scale to large-scale heterogeneous graph data and hindering the application and promotion of these methods. In this paper, we propose Restage, a relation structure-aware hierarchical heterogeneous graph embedding framework. Under this framework, embedding only a smaller-scale graph with existing graph representation learning methods is sufficient to obtain node representations on the original heterogeneous graph. We consider two types of relation structures in heterogeneous graphs: interaction relations and affiliation relations. Firstly, we design a relation structure-aware coarsening method to successively coarsen the original graph to the top-level layer, resulting in a smaller-scale graph. Secondly, we allow any unsupervised representation learning methods to obtain node embeddings on the top-level graph. Finally, we design a relation structure-aware refinement method to successively refine the node embeddings from the top-level graph back to the original graph, obtaining node embeddings on the original graph. Experimental results on three public heterogeneous graph datasets demonstrate the enhanced scalability of representation learning methods by the proposed Restage. On another large-scale graph, the speed of existing representation learning methods is increased by up to eighteen times at most.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"198-214"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Diffusion models are a type of generative deep learning model that can process medical images more efficiently than traditional generative models. They have been applied to several medical image computing tasks. This paper aims to help researchers understand the advancements of diffusion models in medical image computing. It begins by describing the fundamental principles, sampling methods, and architecture of diffusion models. Subsequently, it discusses the application of diffusion models in five medical image computing tasks: image generation, modality conversion, image segmentation, image denoising, and anomaly detection. Additionally, this paper conducts fine-tuning of a large model for image generation tasks and comparative experiments between diffusion models and traditional generative models across these five tasks. The evaluation of the fine-tuned large model shows its potential for clinical applications. Comparative experiments demonstrate that diffusion models have a distinct advantage in tasks related to image generation, modality conversion, and image denoising. However, they require further optimization in image segmentation and anomaly detection tasks to match the efficacy of traditional models. Our codes are publicly available at: https://github.com/hiahub/CodeForDiffusion.
{"title":"Diffusion Models for Medical Image Computing: A Survey","authors":"Yaqing Shi;Abudukelimu Abulizi;Hao Wang;Ke Feng;Nihemaiti Abudukelimu;Youli Su;Halidanmu Abudukelimu","doi":"10.26599/TST.2024.9010047","DOIUrl":"https://doi.org/10.26599/TST.2024.9010047","url":null,"abstract":"Diffusion models are a type of generative deep learning model that can process medical images more efficiently than traditional generative models. They have been applied to several medical image computing tasks. This paper aims to help researchers understand the advancements of diffusion models in medical image computing. It begins by describing the fundamental principles, sampling methods, and architecture of diffusion models. Subsequently, it discusses the application of diffusion models in five medical image computing tasks: image generation, modality conversion, image segmentation, image denoising, and anomaly detection. Additionally, this paper conducts fine-tuning of a large model for image generation tasks and comparative experiments between diffusion models and traditional generative models across these five tasks. The evaluation of the fine-tuned large model shows its potential for clinical applications. Comparative experiments demonstrate that diffusion models have a distinct advantage in tasks related to image generation, modality conversion, and image denoising. However, they require further optimization in image segmentation and anomaly detection tasks to match the efficacy of traditional models. Our codes are publicly available at: https://github.com/hiahub/CodeForDiffusion.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"357-383"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.26599/TST.2024.9010024
Binbin Feng;Zhijun Ding
Workload prediction is critical in enabling proactive resource management of cloud applications. Accurate workload prediction is valuable for cloud users and providers as it can effectively guide many practices, such as performance assurance, cost reduction, and energy consumption optimization. However, cloud workload prediction is highly challenging due to the complexity and dynamics of workloads, and various solutions have been proposed to enhance the prediction behavior. This paper aims to provide an in-depth understanding and categorization of existing solutions through extensive literature reviews. Unlike existing surveys, for the first time, we comprehensively sort out and analyze the development landscape of workload prediction from a new perspective, i.e., application-oriented rather than prediction methodologies per se. Specifically, we first introduce the basic features of workload prediction, and then analyze and categorize existing efforts based on two significant characteristics of cloud applications: variability and heterogeneity. Furthermore, we also investigate how workload prediction is applied to resource management. Finally, open research opportunities in workload prediction are highlighted to foster further advancements.
{"title":"Application-Oriented Cloud Workload Prediction: A Survey and New Perspectives","authors":"Binbin Feng;Zhijun Ding","doi":"10.26599/TST.2024.9010024","DOIUrl":"https://doi.org/10.26599/TST.2024.9010024","url":null,"abstract":"Workload prediction is critical in enabling proactive resource management of cloud applications. Accurate workload prediction is valuable for cloud users and providers as it can effectively guide many practices, such as performance assurance, cost reduction, and energy consumption optimization. However, cloud workload prediction is highly challenging due to the complexity and dynamics of workloads, and various solutions have been proposed to enhance the prediction behavior. This paper aims to provide an in-depth understanding and categorization of existing solutions through extensive literature reviews. Unlike existing surveys, for the first time, we comprehensively sort out and analyze the development landscape of workload prediction from a new perspective, i.e., application-oriented rather than prediction methodologies per se. Specifically, we first introduce the basic features of workload prediction, and then analyze and categorize existing efforts based on two significant characteristics of cloud applications: variability and heterogeneity. Furthermore, we also investigate how workload prediction is applied to resource management. Finally, open research opportunities in workload prediction are highlighted to foster further advancements.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"34-54"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676402","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.26599/TST.2024.9010063
Yibo Dong;Jin Liu;Jiaqi Ren;Zhe Li;Weili Li
Due to our increasing dependence on infrastructure networks, the attack and defense game in these networks has draw great concerns from security agencies. Moreover, when it comes to evaluating the payoffs in practical attack and defense games in infrastructure networks, the lack of consideration for the fuzziness and uncertainty of subjective human judgment brings forth significant challenges to the analysis of strategic interactions among decision makers. This paper employs intuitionistic fuzzy sets (IFSs) to depict such uncertain payoffs, and introduce a theoretical framework for analyzing the attack and defense game in infrastructure networks based on intuitionistic fuzzy theory. We take the changes in three complex network metrics as the universe of discourse, and intuitionistic fuzzy sets are employed based on this universe of discourse to reflect the satisfaction of decision makers. We employ an algorithm based on intuitionistic fuzzy theory to find the Nash equilibrium, and conduct experiments on both local and global networks. Results show that: (1) the utilization of intuitionistic fuzzy sets to depict the payoffs of attack and defense games in infrastructure networks can reflect the unique characteristics of decision makers' subjective preferences. (2) the use of differently weighted proportions of the three complex network metrics has little impact on decision makers' choices of different strategies.
{"title":"Attack and Defense Game with Intuitionistic Fuzzy Payoffs in Infrastructure Networks","authors":"Yibo Dong;Jin Liu;Jiaqi Ren;Zhe Li;Weili Li","doi":"10.26599/TST.2024.9010063","DOIUrl":"https://doi.org/10.26599/TST.2024.9010063","url":null,"abstract":"Due to our increasing dependence on infrastructure networks, the attack and defense game in these networks has draw great concerns from security agencies. Moreover, when it comes to evaluating the payoffs in practical attack and defense games in infrastructure networks, the lack of consideration for the fuzziness and uncertainty of subjective human judgment brings forth significant challenges to the analysis of strategic interactions among decision makers. This paper employs intuitionistic fuzzy sets (IFSs) to depict such uncertain payoffs, and introduce a theoretical framework for analyzing the attack and defense game in infrastructure networks based on intuitionistic fuzzy theory. We take the changes in three complex network metrics as the universe of discourse, and intuitionistic fuzzy sets are employed based on this universe of discourse to reflect the satisfaction of decision makers. We employ an algorithm based on intuitionistic fuzzy theory to find the Nash equilibrium, and conduct experiments on both local and global networks. Results show that: (1) the utilization of intuitionistic fuzzy sets to depict the payoffs of attack and defense games in infrastructure networks can reflect the unique characteristics of decision makers' subjective preferences. (2) the use of differently weighted proportions of the three complex network metrics has little impact on decision makers' choices of different strategies.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"384-401"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676403","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.26599/TST.2023.9010157
Hao Pan;Lili Qiu
Low Earth Orbit (LEO) satellite communication is vital for wireless systems. The main challenges in designing LEO satellite ground stations include increasing the input signal strength to counteract severe path loss, and adaptively steering the direction of the output signal to accommodate the continuous movement of LEO satellites. To overcome these challenges, we present a novel transceiver system, referred to as MetaLEO. This system integrates a passive metasurface with a small phased array, enabling powerful focusing and adaptive signal steering. By harnessing the metasurface's robust wavefront manipulation capabilities and the programmability of phased arrays, MetaLEO offers an efficient and cost-effective solution that supports both uplink and downlink bands. Specifically, we devise a joint optimization model specifically to obtain the optimal uplink codebook for phased array antennas and metasurface phase profile, which enables electronic steering. In a similar manner, we establish the downlink metasurface phase profile to enhance focusing and signal reception. MetaLEO's efficacy is evaluated via theoretical analysis, simulations, and experiments. Our prototype includes a single metasurface with 21×21 uplink and 22×22 downlink elements, and a 1×4 antenna array for receiving and transmitting. Experimental results show signal strength improvements of 8.32 dB (uplink) and 16.57 dB (downlink).
{"title":"Passive Metasurface-Based Low Earth Orbit Ground Station Design","authors":"Hao Pan;Lili Qiu","doi":"10.26599/TST.2023.9010157","DOIUrl":"https://doi.org/10.26599/TST.2023.9010157","url":null,"abstract":"Low Earth Orbit (LEO) satellite communication is vital for wireless systems. The main challenges in designing LEO satellite ground stations include increasing the input signal strength to counteract severe path loss, and adaptively steering the direction of the output signal to accommodate the continuous movement of LEO satellites. To overcome these challenges, we present a novel transceiver system, referred to as MetaLEO. This system integrates a passive metasurface with a small phased array, enabling powerful focusing and adaptive signal steering. By harnessing the metasurface's robust wavefront manipulation capabilities and the programmability of phased arrays, MetaLEO offers an efficient and cost-effective solution that supports both uplink and downlink bands. Specifically, we devise a joint optimization model specifically to obtain the optimal uplink codebook for phased array antennas and metasurface phase profile, which enables electronic steering. In a similar manner, we establish the downlink metasurface phase profile to enhance focusing and signal reception. MetaLEO's efficacy is evaluated via theoretical analysis, simulations, and experiments. Our prototype includes a single metasurface with 21×21 uplink and 22×22 downlink elements, and a 1×4 antenna array for receiving and transmitting. Experimental results show signal strength improvements of 8.32 dB (uplink) and 16.57 dB (downlink).","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"148-160"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676348","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The exponential advancement witnessed in 5G communication and quantum computing has presented unparalleled prospects for safeguarding sensitive data within healthcare infrastructures. This study proposes a novel framework for healthcare applications that integrates 5G communication, quantum computing, and sensitive data measurement to address the challenges of measuring and securely transmitting sensitive medical data. The framework includes a quantum-inspired method for quantifying data sensitivity based on quantum superposition and entanglement principles and a delegated quantum computing protocol for secure data transmission in 5G-enabled healthcare systems, ensuring user anonymity and data confidentiality. The framework is applied to innovative healthcare scenarios, such as secure 5G voice communication, data transmission, and short message services. Experimental results demonstrate the framework's high accuracy in sensitive data measurement and enhanced security for data transmission in 5G healthcare systems, surpassing existing approaches.
{"title":"Quantum-Inspired Sensitive Data Measurement and Secure Transmission in 5G-Enabled Healthcare Systems","authors":"Xiaohong Lv;Shalli Rani;Shanmuganathan Manimurugan;Adam Slowik;Yanhong Feng","doi":"10.26599/TST.2024.9010122","DOIUrl":"https://doi.org/10.26599/TST.2024.9010122","url":null,"abstract":"The exponential advancement witnessed in 5G communication and quantum computing has presented unparalleled prospects for safeguarding sensitive data within healthcare infrastructures. This study proposes a novel framework for healthcare applications that integrates 5G communication, quantum computing, and sensitive data measurement to address the challenges of measuring and securely transmitting sensitive medical data. The framework includes a quantum-inspired method for quantifying data sensitivity based on quantum superposition and entanglement principles and a delegated quantum computing protocol for secure data transmission in 5G-enabled healthcare systems, ensuring user anonymity and data confidentiality. The framework is applied to innovative healthcare scenarios, such as secure 5G voice communication, data transmission, and short message services. Experimental results demonstrate the framework's high accuracy in sensitive data measurement and enhanced security for data transmission in 5G healthcare systems, surpassing existing approaches.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"456-478"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676360","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.26599/TST.2024.9010020
Wenji He;Haipeng Yao;Huan Chang;Yunjie Liu
With various service types including massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC), fifth generation (5G) networks require advanced resources management strategies. As a method to segment network resources logically, network slicing (NS) addresses the challenges of heterogeneity and scalability prevalent in these networks. Traditional software-defined networking (SDN) technologies, lack the flexibility needed for precise control over network resources and fine-grained packet management. This has led to significant developments in programmable switches, with programming protocol-independent packet processors (P4) emerging as a transformative programming language. P4 endows network devices with flexibility and programmability, overcoming traditional SDN limitations and enabling more dynamic, precise network slicing implementations. In our work, we leverage the capabilities of P4 to forge a groundbreaking closed-loop architecture that synergizes the programmable data plane with an intelligent control plane. We set up a token bucket-based bandwidth management and traffic isolation mechanism in the data plane, and use the generative diffusion model to generate the key configuration of the strategy in the control plane. Through comprehensive experimentation, we validate the effectiveness of our architecture, underscoring its potential as a significant advancement in 5G network traffic management.
第五代（5G）网络拥有多种服务类型，包括大规模机器型通信（mMTC）和超可靠低延迟通信（URLLC），因此需要先进的资源管理策略。作为一种对网络资源进行逻辑分割的方法，网络切片（NS）解决了这些网络中普遍存在的异构性和可扩展性挑战。传统的软件定义网络（SDN）技术缺乏精确控制网络资源和细粒度数据包管理所需的灵活性。这促使可编程交换机取得了重大发展，与编程协议无关的数据包处理器（P4）成为一种变革性的编程语言。P4 赋予网络设备灵活性和可编程性，克服了传统的 SDN 限制，实现了更动态、更精确的网络切片。在我们的工作中，我们利用 P4 的功能打造了一个开创性的闭环架构，将可编程数据平面与智能控制平面协同起来。我们在数据平面建立了基于令牌桶的带宽管理和流量隔离机制，并在控制平面使用生成式扩散模型生成策略的关键配置。通过全面的实验，我们验证了我们的架构的有效性，强调了其作为 5G 网络流量管理的重要进步的潜力。
{"title":"A P4-Based Approach to Traffic Isolation and Bandwidth Management for 5G Network Slicing","authors":"Wenji He;Haipeng Yao;Huan Chang;Yunjie Liu","doi":"10.26599/TST.2024.9010020","DOIUrl":"https://doi.org/10.26599/TST.2024.9010020","url":null,"abstract":"With various service types including massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC), fifth generation (5G) networks require advanced resources management strategies. As a method to segment network resources logically, network slicing (NS) addresses the challenges of heterogeneity and scalability prevalent in these networks. Traditional software-defined networking (SDN) technologies, lack the flexibility needed for precise control over network resources and fine-grained packet management. This has led to significant developments in programmable switches, with programming protocol-independent packet processors (P4) emerging as a transformative programming language. P4 endows network devices with flexibility and programmability, overcoming traditional SDN limitations and enabling more dynamic, precise network slicing implementations. In our work, we leverage the capabilities of P4 to forge a groundbreaking closed-loop architecture that synergizes the programmable data plane with an intelligent control plane. We set up a token bucket-based bandwidth management and traffic isolation mechanism in the data plane, and use the generative diffusion model to generate the key configuration of the strategy in the control plane. Through comprehensive experimentation, we validate the effectiveness of our architecture, underscoring its potential as a significant advancement in 5G network traffic management.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"171-185"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676349","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.26599/TST.2024.9010040
Runjie Miao;Chenchen Wu;Jinjiang Yuan
Capacitated facility location problem (CFLP) is a classical combinatorial optimization problem that has various applications in operations research, theoretical computer science, and management science. In the CFLP, we have a potential facilities set and a clients set. Each facility has a certain capacity and an open cost, and each client has a spliitable demand that need to be met. The goal is to open some facilities and assign all clients to these open facilities so that the total cost is as low as possible. The CFLP is NP-hard (non-deterministic polynomial-hard), and a large amount of work has been devoted to designing approximation algorithms for CFLP and its variants. Following this vein, we introduce a new variant of CFLP called capacitated uniform facility location problem with soft penalties (CUFLPSP), in which the demand of each client can be partially rejected by paying penalty costs. As a result, we present a linear programming-rounding (LP-rounding) based 5.5122-approximation algorithm for the CUFLPSP.
{"title":"LP-Rounding Based Algorithm for Capacitated Uniform Facility Location Problem with Soft Penalties","authors":"Runjie Miao;Chenchen Wu;Jinjiang Yuan","doi":"10.26599/TST.2024.9010040","DOIUrl":"https://doi.org/10.26599/TST.2024.9010040","url":null,"abstract":"Capacitated facility location problem (CFLP) is a classical combinatorial optimization problem that has various applications in operations research, theoretical computer science, and management science. In the CFLP, we have a potential facilities set and a clients set. Each facility has a certain capacity and an open cost, and each client has a spliitable demand that need to be met. The goal is to open some facilities and assign all clients to these open facilities so that the total cost is as low as possible. The CFLP is NP-hard (non-deterministic polynomial-hard), and a large amount of work has been devoted to designing approximation algorithms for CFLP and its variants. Following this vein, we introduce a new variant of CFLP called capacitated uniform facility location problem with soft penalties (CUFLPSP), in which the demand of each client can be partially rejected by paying penalty costs. As a result, we present a linear programming-rounding (LP-rounding) based 5.5122-approximation algorithm for the CUFLPSP.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"279-289"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676352","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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