Pub Date : 2025-12-23DOI: 10.1109/tsc.2025.3647522
Fan Yang, Zhengyang Huang, Zimo Wen, Lvyang Ye, Yanan Qiao
{"title":"Blockchain-Empowered AI for Fintech Services Computing: A Verifiable Framework for Transparent and Sustainable Credit Risk Assessment","authors":"Fan Yang, Zhengyang Huang, Zimo Wen, Lvyang Ye, Yanan Qiao","doi":"10.1109/tsc.2025.3647522","DOIUrl":"https://doi.org/10.1109/tsc.2025.3647522","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813211","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}
Pub Date : 2025-12-23DOI: 10.1109/TSC.2025.3647667
Sheng Zhu;Xuege Han;Yiqian Zhao;Jinting Wang
The escalating security risks in hybrid cloud environments present a critical bottleneck to broader adoption. This study proposes an innovative risk management strategy to maximize provider revenue. By modeling the cloud service architecture as a queueing system, where user requests are “customers” and resources are “servers”, we quantitatively analyze user decision-making under dynamic risk perceptions. Integrating queueing game theory with a Stackelberg framework, we systematically model the customer trade-off between service efficiency and security assurance. Our analysis reveals Nash equilibrium patterns under diverse data breach liability allocations, deriving an optimized provider risk-control model. This strategy guides market behavior through policy optimization to achieve risk-revenue equilibrium. The work advances theoretical foundations for hybrid cloud risk mitigation and provides actionable strategies for cloud providers.
{"title":"Balancing Service Efficiency and Data Security in Hybrid Cloud Systems: A Queueing Game Approach","authors":"Sheng Zhu;Xuege Han;Yiqian Zhao;Jinting Wang","doi":"10.1109/TSC.2025.3647667","DOIUrl":"10.1109/TSC.2025.3647667","url":null,"abstract":"The escalating security risks in hybrid cloud environments present a critical bottleneck to broader adoption. This study proposes an innovative risk management strategy to maximize provider revenue. By modeling the cloud service architecture as a queueing system, where user requests are “customers” and resources are “servers”, we quantitatively analyze user decision-making under dynamic risk perceptions. Integrating queueing game theory with a Stackelberg framework, we systematically model the customer trade-off between service efficiency and security assurance. Our analysis reveals Nash equilibrium patterns under diverse data breach liability allocations, deriving an optimized provider risk-control model. This strategy guides market behavior through policy optimization to achieve risk-revenue equilibrium. The work advances theoretical foundations for hybrid cloud risk mitigation and provides actionable strategies for cloud providers.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 1","pages":"826-832"},"PeriodicalIF":5.8,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813212","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}
Pub Date : 2025-12-18DOI: 10.1109/TSC.2025.3645803
Tianzi Zhao;Xinran Liu;Zhaoxin Zhang;Dong Zhao;Ning Li;Zhichao Zhang;Xinye Wang;Tiantian Ji
Fine-grained IP geolocation plays a critical role in location-based services and cybersecurity. However, due to the impact of inherent noise on data features, most existing fine-grained IP geolocation methods fail to accurately predict the region of the target host. Although recent studies have focused on IP region prediction, they do not provide reliability assessments for the predictions, which limits the practical application of IP geolocation. To address these challenges, this paper proposes UncertaintyGeo, a fine-grained IP geolocation framework based on the Dirichlet network. We revisit IP geolocation from a classification perspective and introduce a “region-first, coordinate-second” paradigm rooted in the Dirichlet distribution. This framework first predicts the region of the target host, and uses the properties of the Dirichlet concentration parameters to evaluate the reliability of the predictions, then obtains coordinate predictions by weighting the centers of candidate regions based on prediction confidence. Experiments on real-world datasets from New York, Los Angeles, and Shanghai demonstrate that UncertaintyGeo significantly outperforms state-of-the-art methods in region-level prediction while achieving notable advantages in coordinate-level prediction and reliability estimation.
{"title":"UncertaintyGeo: A Dirichlet Network Architecture for Evaluating IP Geolocation Uncertainty","authors":"Tianzi Zhao;Xinran Liu;Zhaoxin Zhang;Dong Zhao;Ning Li;Zhichao Zhang;Xinye Wang;Tiantian Ji","doi":"10.1109/TSC.2025.3645803","DOIUrl":"10.1109/TSC.2025.3645803","url":null,"abstract":"Fine-grained IP geolocation plays a critical role in location-based services and cybersecurity. However, due to the impact of inherent noise on data features, most existing fine-grained IP geolocation methods fail to accurately predict the region of the target host. Although recent studies have focused on IP region prediction, they do not provide reliability assessments for the predictions, which limits the practical application of IP geolocation. To address these challenges, this paper proposes UncertaintyGeo, a fine-grained IP geolocation framework based on the Dirichlet network. We revisit IP geolocation from a classification perspective and introduce a “region-first, coordinate-second” paradigm rooted in the Dirichlet distribution. This framework first predicts the region of the target host, and uses the properties of the Dirichlet concentration parameters to evaluate the reliability of the predictions, then obtains coordinate predictions by weighting the centers of candidate regions based on prediction confidence. Experiments on real-world datasets from New York, Los Angeles, and Shanghai demonstrate that UncertaintyGeo significantly outperforms state-of-the-art methods in region-level prediction while achieving notable advantages in coordinate-level prediction and reliability estimation.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 1","pages":"766-779"},"PeriodicalIF":5.8,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778086","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}
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