IEEE Transactions on Services Computing最新文献_第9页

Flexible Computing: A New Framework for Improving Resource Allocation and Scheduling in Elastic Computing 灵活计算：改进弹性计算中资源分配和调度的新框架

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-31 DOI: 10.1109/TSC.2024.3489433

Weipeng Cao;Jiongjiong Gu;Zhong Ming;Zhiyuan Cai;Yuzhao Wang;Changping Ji;Zhijiao Xiao;Yuhong Feng;Ye Liu;Liang-Jie Zhang

{"title":"Flexible Computing: A New Framework for Improving Resource Allocation and Scheduling in Elastic Computing","authors":"Weipeng Cao;Jiongjiong Gu;Zhong Ming;Zhiyuan Cai;Yuzhao Wang;Changping Ji;Zhijiao Xiao;Yuhong Feng;Ye Liu;Liang-Jie Zhang","doi":"10.1109/TSC.2024.3489433","DOIUrl":"10.1109/TSC.2024.3489433","url":null,"abstract":"Since the advent of cloud computing, Elastic Computing (EC) has become the standard architecture for resource allocation and scheduling. EC typically allocates computing resources based on predefined specifications, such as virtual machine or container flavors. However, these flavors are often constrained by fixed CPU-to-memory ratios, which frequently fail to match the actual resource needs of applications. As a result, cloud providers experience high resource allocation rates nearing saturation (<inline-formula><tex-math>$> $</tex-math></inline-formula>80%) but with low utilization (<inline-formula><tex-math>$< $</tex-math></inline-formula>25%). This study introduces Flexible Computing (FC), a novel approach to resource allocation and scheduling. Unlike EC, FC allocates resources based on an application resource usage profile, derived from the historical resource consumption of workloads, rather than relying on fixed specifications. Additionally, FC incorporates a real-time performance degradation detection mechanism to address performance issues caused by the noisy-neighbor effect when colocated workloads interfere with each other. FC dynamically adjusts resource allocation according to actual usage, ensuring that application performance meets Service Level Agreements (SLAs), while preventing resource waste and performance degradation from improper resource over-commitment. Large-scale experimental validations conducted on the FC architecture within Huawei Cloud data centers demonstrate that, compared to EC, FC can reduce computing resource consumption by over 33% while managing the same workloads. Furthermore, FC's real-time performance degradation detection model achieves a prediction error of less than 5% across various testing environments, highlighting its commercial viability.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"198-211"},"PeriodicalIF":5.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

No More Data Silos: Unified Microservice Failure Diagnosis With Temporal Knowledge Graph 不再有数据孤岛：利用时态知识图谱统一微服务故障诊断

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-31 DOI: 10.1109/TSC.2024.3489444

Shenglin Zhang;Yongxin Zhao;Sibo Xia;Shirui Wei;Yongqian Sun;Chenyu Zhao;Shiyu Ma;Junhua Kuang;Bolin Zhu;Lemeng Pan;Yicheng Guo;Dan Pei

{"title":"No More Data Silos: Unified Microservice Failure Diagnosis With Temporal Knowledge Graph","authors":"Shenglin Zhang;Yongxin Zhao;Sibo Xia;Shirui Wei;Yongqian Sun;Chenyu Zhao;Shiyu Ma;Junhua Kuang;Bolin Zhu;Lemeng Pan;Yicheng Guo;Dan Pei","doi":"10.1109/TSC.2024.3489444","DOIUrl":"10.1109/TSC.2024.3489444","url":null,"abstract":"Microservices improve the scalability and flexibility of monolithic architectures to accommodate the evolution of software systems, but the complexity and dynamics of microservices challenge system reliability. Ensuring microservice quality requires efficient failure diagnosis, including detection and triage. Failure detection involves identifying anomalous behavior within the system, while triage entails classifying the failure type and directing it to the engineering team for resolution. Unfortunately, current approaches reliant on single-modal monitoring data, such as metrics, logs, or traces, cannot capture all failures and neglect interconnections among multimodal data, leading to erroneous diagnoses. Recent multimodal data fusion studies struggle to achieve deep integration, limiting diagnostic accuracy due to insufficiently captured interdependencies. Therefore, we propose \u0000<italic>UniDiag\u0000, which leverages temporal knowledge graphs to fuse multimodal data for effective failure diagnosis. \u0000<italic>UniDiag\u0000 applies a simple yet effective stream-based anomaly detection method to reduce computational cost and a novel microservice-oriented graph embedding method to represent the state of systems comprehensively. To assess the performance of \u0000<italic>UniDiag\u0000, we conduct extensive evaluation experiments using datasets from two benchmark microservice systems, demonstrating its superiority over existing methods and affirming the efficacy of multimodal data fusion. Additionally, we have publicly made the code and data available to facilitate further research.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"4013-4026"},"PeriodicalIF":5.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Public-Key Searchable Encryption Scheme From PSI With Scalable Proxy Servers 具有可扩展代理服务器的 PSI 高效公钥可搜索加密方案

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-31 DOI: 10.1109/TSC.2024.3489432

Xiangqian Kong;Lanxiang Chen;Yizhao Zhu;Yi Mu

{"title":"Efficient Public-Key Searchable Encryption Scheme From PSI With Scalable Proxy Servers","authors":"Xiangqian Kong;Lanxiang Chen;Yizhao Zhu;Yi Mu","doi":"10.1109/TSC.2024.3489432","DOIUrl":"10.1109/TSC.2024.3489432","url":null,"abstract":"Public-key Encryption with Keyword Search (PEKS) enables secure keyword searches within encrypted data. At the same time, Public-key Authenticated Encryption with Keyword Search (PAEKS) enhances security by permitting authorized users to search specific keyword sets, protecting against Internal Keyword Guessing Attacks (IKGA). However, to the best of our knowledge, existing PEKS and PAEKS schemes typically require to generate a distinct set of keyword ciphertext for each data user, leading to storage, computation, and communication costs and the lack of support for multiple-keyword search. In this article, we introduce a novel, efficient public-key searchable encryption scheme from the private set intersection (PSI) with scalable proxy servers, using a PSI protocol with multiple proxy server settings, which achieves sub-linear complexity. Our scheme is secure against IKGA and supports multiple keyword searches and sharing one encrypted keyword set by multiple users. We introduce an efficient system model with scalable proxy servers, significantly reducing computational overhead through a divide-and-conquer approach. Our proposed scheme supports multiple data users, and multiple keyword searches, utilizing a single set of keyword ciphertext for multiple data users. We formally define a security model and present a comprehensive security proof to demonstrate that our scheme maintains ciphertext-indistinguishability and trapdoor-indistinguishability.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3527-3540"},"PeriodicalIF":5.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Objective Deep Reinforcement Learning for Function Offloading in Serverless Edge Computing 用于无服务器边缘计算功能卸载的多目标深度强化学习

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-31 DOI: 10.1109/TSC.2024.3489443

Yaning Yang;Xiao Du;Yutong Ye;Jiepin Ding;Ting Wang;Mingsong Chen;Keqin Li

{"title":"Multi-Objective Deep Reinforcement Learning for Function Offloading in Serverless Edge Computing","authors":"Yaning Yang;Xiao Du;Yutong Ye;Jiepin Ding;Ting Wang;Mingsong Chen;Keqin Li","doi":"10.1109/TSC.2024.3489443","DOIUrl":"10.1109/TSC.2024.3489443","url":null,"abstract":"Function offloading problems play a crucial role in optimizing the performance of applications in serverless edge computing (SEC). Existing research has extensively explored function offloading strategies based on optimizing a single objective. However, a significant challenge arises when users expect to optimize multiple objectives according to the relative importance of these objectives. This challenge becomes particularly pronounced when the relative importance of the objectives dynamically shifts. Consequently, there is an urgent need for research into multi-objective function offloading methods. In this paper, we redefine the SEC function offloading problem as a dynamic multi-objective optimization issue and propose a novel approach based on Multi-objective Reinforcement Learning (MORL) called MOSEC. MOSEC can coordinately optimize three objectives, i.e., application completion time, User Device (UD) energy consumption, and user cost. To reduce the impact of extrapolation errors, MOSEC integrates a Near-on Experience Replay (NER) strategy during the model training. Furthermore, MOSEC adopts our proposed Earliest First (EF) scheme to maintain the policies learned previously, which can efficiently mitigate the catastrophic policy forgetting problem. Extensive experiments conducted on various generated applications demonstrate the superiority of MOSEC over state-of-the-art multi-objective optimization algorithms.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"288-301"},"PeriodicalIF":5.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeFiGuard: A Price Manipulation Detection Service in DeFi Using Graph Neural Networks DeFiGuard：使用图神经网络的 DeFi 价格操纵检测服务

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-31 DOI: 10.1109/TSC.2024.3489439

Dabao Wang;Bang Wu;Xingliang Yuan;Lei Wu;Yajin Zhou;Helei Cui

{"title":"DeFiGuard: A Price Manipulation Detection Service in DeFi Using Graph Neural Networks","authors":"Dabao Wang;Bang Wu;Xingliang Yuan;Lei Wu;Yajin Zhou;Helei Cui","doi":"10.1109/TSC.2024.3489439","DOIUrl":"10.1109/TSC.2024.3489439","url":null,"abstract":"The prosperity of Decentralized Finance (DeFi) unveils underlying risks, with reported losses surpassing 3.2 billion USD between 2018 and 2022 due to vulnerabilities in Decentralized Applications (DApps). One significant threat is the Price Manipulation Attack (PMA) that alters asset prices during transaction execution. As a result, PMA accounts for over 50 million USD in losses. To address the urgent need for efficient PMA detection, this article introduces a novel detection service, \u0000<italic>DeFiGuard\u0000, using Graph Neural Networks (GNNs). In this article, we propose cash flow graphs with four distinct features, which capture the trading behaviors from transactions. Moreover, \u0000<italic>DeFiGuard\u0000 integrates transaction parsing, graph construction, model training, and PMA detection. Evaluations on the collected transactions demonstrate that \u0000<italic>DeFiGuard\u0000 with GNN models outperforms the baseline MLP model and classical classification models in Accuracy, TPR, FPR, and AUC-ROC. The results of ablation studies suggest that the combination of the four proposed node features enhances \u0000<italic>DeFiGuard\u0000 ’s efficacy. Moreover, \u0000<italic>DeFiGuard\u0000 classifies transactions within 0.892 to 5.317 seconds, which provides sufficient time for the victims (DApps and users) to take action to rescue their vulnerable funds. In conclusion, this research offers a significant step towards safeguarding the DeFi landscape from PMAs using GNNs.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3345-3358"},"PeriodicalIF":5.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Light Heterogeneous Hypergraph Contrastive Learning Based Service Recommendation for Mashup Creation 基于轻型异构超图对比学习的混搭创建服务推荐

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-31 DOI: 10.1109/TSC.2024.3489417

Mingdong Tang;Jiajin Mai;Fenfang Xie;Zibin Zheng

{"title":"Light Heterogeneous Hypergraph Contrastive Learning Based Service Recommendation for Mashup Creation","authors":"Mingdong Tang;Jiajin Mai;Fenfang Xie;Zibin Zheng","doi":"10.1109/TSC.2024.3489417","DOIUrl":"10.1109/TSC.2024.3489417","url":null,"abstract":"Mashup technology enables developers to create new applications more readily by combining existing services. As its popularity grows, research on service recommendation for mashup creation has gained increasing attention. Existing recommendation methods have the following limitations: either they are susceptible to data sparsity problems, or they exhibit over-smoothing when aggregating high-order neighbors, resulting in similar and non-specific node feature representations, or they only focus on bipartite graphs and neglect the rich heterogeneous information in the mashup-service ecosystem. To address these issues, we propose a service recommendation method for mashup creation based on \u0000<underline>l\u0000ight \u0000<underline>h\u0000eterogeneous hyper\u0000<underline>g\u0000raph \u0000<underline>c\u0000ontrastive \u0000<underline>l\u0000earning (LHGCL). This method first constructs a heterogeneous hypergraph by combining mashup information, service information, the mashup-service interaction data, and their related attribute information. Then, it designs a light hypergraph neural network to capture the high-order relationships between mashups and services. Next, it applies contrastive learning to enhance the representations of mashups and services. Finally, it utilizes the enhanced feature vectors of mashups and services to predict mashup preferences for services. Comprehensive experiments conducted on the real-world ProgrammableWeb dataset demonstrate the superiority of the proposed method and the effectiveness of its key modules.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3844-3856"},"PeriodicalIF":5.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continuous Management of Machine Learning-Based Application Behavior 持续管理基于机器学习的应用程序行为

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-28 DOI: 10.1109/TSC.2024.3486226

Marco Anisetti;Claudio A. Ardagna;Nicola Bena;Ernesto Damiani;Paolo G. Panero

{"title":"Continuous Management of Machine Learning-Based Application Behavior","authors":"Marco Anisetti;Claudio A. Ardagna;Nicola Bena;Ernesto Damiani;Paolo G. Panero","doi":"10.1109/TSC.2024.3486226","DOIUrl":"10.1109/TSC.2024.3486226","url":null,"abstract":"Modern applications are increasingly driven by Machine Learning (ML) models whose non-deterministic behavior is affecting the entire application life cycle from design to operation. The pervasive adoption of ML is urgently calling for approaches that guarantee a stable non-functional behavior of ML-based applications over time and across model changes. To this aim, non-functional properties of ML models, such as privacy, confidentiality, fairness, and explainability, must be monitored, verified, and maintained. Existing approaches mostly focus on <italic>i) implementing solutions for classifier selection according to the functional behavior of ML models, <italic>ii) finding new algorithmic solutions, such as continuous re-training. In this paper, we propose a multi-model approach that aims to guarantee a stable non-functional behavior of ML-based applications. An architectural and methodological approach is provided to compare multiple ML models showing similar non-functional properties and select the model supporting stable non-functional behavior over time according to (dynamic and unpredictable) contextual changes. Our approach goes beyond the state of the art by providing a solution that continuously guarantees a stable non-functional behavior of ML-based applications, is ML algorithm-agnostic, and is driven by non-functional properties assessed on the ML models themselves. It consists of a two-step process working during application operation, where <italic>model assessment verifies non-functional properties of ML models trained and selected at development time, and <italic>model substitution guarantees continuous and stable support of non-functional properties. We experimentally evaluate our solution in a real-world scenario focusing on non-functional property fairness.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"112-125"},"PeriodicalIF":5.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

Optimized Edge Node Allocation Considering User Delay Tolerance for Cost Reduction 考虑用户延迟容忍度的优化边缘节点分配以降低成本

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-28 DOI: 10.1109/TSC.2024.3486174

Xiaoyu Zhang;Shixun Huang;Hai Dong;Zhifeng Bao;Jiajun Liu;Xun Yi

{"title":"Optimized Edge Node Allocation Considering User Delay Tolerance for Cost Reduction","authors":"Xiaoyu Zhang;Shixun Huang;Hai Dong;Zhifeng Bao;Jiajun Liu;Xun Yi","doi":"10.1109/TSC.2024.3486174","DOIUrl":"10.1109/TSC.2024.3486174","url":null,"abstract":"With the rise of 5G technology, Mobile (or Multi-Access) Edge Computing (MEC) has become crucial in modern network architecture. One key research area is the effective placement of edge nodes, which has attracted significant attention. Service providers strive to minimize deployment costs for these nodes within a network. Although many studies have explored optimal strategies for reducing these costs, most overlook the allocation of computational resources and the users’ tolerance for delays. These factors add complexity, making previous methods less adaptable. In this paper, we define the Cost Minimization in MEC Edge Node Placement problem. Our goal is to find the optimal strategy for deploying edge nodes that minimize costs while cater to users’ delay tolerance limits. We prove the NP-hardness of this problem and provide a range of solutions, including Cluster-based Mixed Integer Programming, Coverage First Search, and Distance-Aware Coverage First Search, to address this challenge effectively and efficiently. Additionally, we propose a fine-grained optimization approach for allocating computational resources to edge nodes based on user service requests, significantly lowering deployment costs. Extensive experiments on a large-scale real-world dataset show that our solutions outperform the state-of-the-art in efficiency, effectiveness, and scalability.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"4055-4068"},"PeriodicalIF":5.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lighter Sequential Recommendation Algorithm With Time Interval Awareness Augmentation 增强时间间隔意识的轻量级顺序推荐算法

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-25 DOI: 10.1109/TSC.2024.3479911

Xiaoyao Zheng;Xingwang Li;Shengfei Jiang;Zhenghua Chen;Liping Sun;Qingying Yu;Liangmin Guo;Yonglong Luo

{"title":"Lighter Sequential Recommendation Algorithm With Time Interval Awareness Augmentation","authors":"Xiaoyao Zheng;Xingwang Li;Shengfei Jiang;Zhenghua Chen;Liping Sun;Qingying Yu;Liangmin Guo;Yonglong Luo","doi":"10.1109/TSC.2024.3479911","DOIUrl":"10.1109/TSC.2024.3479911","url":null,"abstract":"Sequential recommendation models analyze users’ historical interactions to predict the next item they will en gage with. In order to better capture users’ dynamic interest preferences, most existing sequential recommendation models that introduce heterogeneous time intervals lead to increased model complexity, which raises computational costs and training difficulty. This is particularly evident in long sequential data, where the model need to handle a large variety of different time intervals. Additionally, accurately modeling the impact of long time intervals on user behavior remains a significant challenge. To address these issues, we propose a lightweight sequential recommendation algorithm with time interval awareness augmen tation (TALSAN). This model introduces a novel uniform data augmentation operator to improve the distribution of original data samples and employs a time-aware self-attention layer to model user interactions, maintaining the continuity of the original sequence. By integrating temporal context with posi tional features, TALSAN constructs a streamlined self-attention network for predicting user behavior. Comparative testing on datasets such as ML-100K, ML-1M, Amazon Beauty, Amazon Toys, and Amazon Fashion demonstrates the model’s superiority over existing baselines. Our results confirm that TALSAN not only mitigates cold start issues but also enhances the ability to learn user preferences, leading to improved prediction accuracy.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3857-3868"},"PeriodicalIF":5.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142490462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Privacy-Preserving Computation Framework for Multisource Label Propagation Services 多源标签传播服务的隐私保护计算框架

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-24 DOI: 10.1109/TSC.2024.3486196

Tanren Liu;Zhuo Ma;Yang Liu;Xin Kang;Bingsheng Zhang;Jianfeng Ma

{"title":"A Privacy-Preserving Computation Framework for Multisource Label Propagation Services","authors":"Tanren Liu;Zhuo Ma;Yang Liu;Xin Kang;Bingsheng Zhang;Jianfeng Ma","doi":"10.1109/TSC.2024.3486196","DOIUrl":"10.1109/TSC.2024.3486196","url":null,"abstract":"Multisource Private Label Propagation (MPLP) is designed for different organizations to collaboratively predict labels of unlabeled nodes through iterative propagation and label updates without revealing sensitive information. Aside from the privacy of the origin data, in some statistical prediction services, it is only needed to learn about the statistical results and concrete prediction results for the abnormal nodes. To do it, we first design a basic MPLP scheme, \u0000PriLP\u0000, to meet the requirements of the privacy of origin data and the concrete prediction of normal nodes. However, our basic achievement of \u0000PriLP\u0000 relies heavily on Additive Homomorphic Encryption (AHE) due to the sparse graph representation in label propagation. To diminish reliance on AHE, our optimization facilitates data encryption in a more compact representation, resulting in encryption times that scale linearly with the number of graph nodes. Our experiments show \u0000PriLP\u0000 closely matches plain-label propagation within \u0000<inline-formula><tex-math>$leq 0.7%$</tex-math></inline-formula>\u0000 difference in accuracy, and the optimizations lead to up to \u0000<inline-formula><tex-math>$22.63times$</tex-math></inline-formula>\u0000 faster execution and \u0000<inline-formula><tex-math>$1.83times$</tex-math></inline-formula>\u0000 less communication than the basic implement.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3078-3091"},"PeriodicalIF":5.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142489483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0