IEEE Transactions on Services Computing最新文献

{"title":"Intelligent Cooperative Computation Offloading and Resource Allocation for Dual-Dependency Tasks in Edge Computing","authors":"Zhixiu Yao;Yun Li;Qilie Liu;Shichao Xia;Yi Jiang","doi":"10.1109/TSC.2026.3709905","DOIUrl":"10.1109/TSC.2026.3709905","url":null,"abstract":"Mobile edge computing (MEC) has accelerated the development of artificial intelligence and Internet of Things technologies, leading to the explosive growth of intelligent applications characterized by resource intensity and latency sensitivity, such as image processing and smart home. In practice, an application typically consists of multiple tasks with execution dependencies, where the output of some tasks serves as the input for specific others. Recently, the design of computation offloading methods for such execution-dependent tasks has received extensive research. However, computation offloading for execution-dependent tasks with service dependencies in resource-constrained multi-user, multi-edge-server cooperative MEC systems has not been thoroughly studied. In this paper, we formulate a cooperative computation offloading problem for dual-dependency tasks in multi-edge-server scenarios with limited service and computing resources, aiming to minimize the long-term average service delay for multiple users. To solve this problem, we propose a recurrent multi-agent reinforcement learning-based dual-dependency task offloading (RMA-DepO) algorithm, which enables users to communicate during training to explore and learn optimal joint task offloading and computing resource allocation strategies, and to make distributed offloading decisions at execution time. Simulation results demonstrate that the proposed RMA-DepO algorithm outperforms several baselines under different network settings, demonstrating its effectiveness in coordinating edge resources for cooperative computation of dual-dependency tasks.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2843-2856"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148383986","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}

{"title":"Microservices Anomaly Detection Based on Adaptive Wavelet Convolutional Networks and Transformer","authors":"Wei Tang;Congfeng Jiang;Guangming Wang;Junming Liu","doi":"10.1109/TSC.2026.3701934","DOIUrl":"https://doi.org/10.1109/TSC.2026.3701934","url":null,"abstract":"Microservice architecture, characterized by loose coupling, elastic scalability, and independent deployment, has become the de facto standard for cloud-native services. However, in production systems, the complex interaction topologies among microservices can easily trigger cascading failures, which severely compromise system reliability and operational stability. This paper proposes an anomaly detection method for microservices that integrates adaptive wavelet convolutional networks and Transformer architecture. Specifically, the proposed method leverages a wavelet convolutional network to preprocess time-series data, while a weighted inverse transformer combined with a frequency-band attention mechanism is utilized to capture multi-scale time–frequency features of different microservices. Moreover, a dual-branch GRU-Transformer structure is then used to model both long- and short-term dependencies as well as cross-metric correlations among services. Therefore, anomaly scores can be derived through the joint optimization of prediction and reconstruction models. To overcome the challenge of threshold determination, this paper also proposes a two-stage gradient-based optimization algorithm that adaptively converges the threshold range using finite differential methods. Experimental results show that the proposed method is effective on datasets such as SMD (Server Machine Dataset), ASD (Application Server Dataset) and the NASA Anomaly Detection Dataset SMAP, where its average F1 score on SMD exceeds that of existing approaches by 11.0%, while on the SMAP dataset, an accuracy of 96.40% is achieved. These results confirm that the proposed approach can effectively detect anomalies in industrial level microservices, thereby enhancing the stability and reliability of industrial service systems.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2929-2942"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11557239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694822","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}

{"title":"Collaborative Knowledge Editing for Large Language Model Services in Edge-Cloud Computing","authors":"Guozeng Xu;Xiuhua Li;Junhao Wen;Qiang He;Xiaofei Wang;Victor C. M. Leung","doi":"10.1109/TSC.2026.3703848","DOIUrl":"10.1109/TSC.2026.3703848","url":null,"abstract":"With the rapid deployment of large language model (LLM) technology across various fields, numerous LLMs have been deployed on edge servers (ESs) to provide low-latency generative services for edge devices. However, as factual knowledge evolves, the massive number of parameters in LLMs poses significant challenges for updating LLMs on ESs. Existing studies employ federated fine-tuning to update LLMs, but these unconstrained updating approaches can lead to overfitting and knowledge forgetting, while also resulting in substantial overhead. Knowledge editing (KE), as a promising technology, ensures the injection of new knowledge while preserving existing knowledge by editing specific parameters. In this paper, we propose multi-ES collaborative KE for the first time and design the CoKE and pCoKE frameworks in edge-cloud scenarios. These frameworks enhance editing efficiency by extracting identical expressions of the same knowledge across different LLM parameters for collaborative editing across multiple ESs. Additionally, we incorporate a personalized selection module in pCoKE to provide domain-specific generative services on ESs. To further reduce editing latency, we design a binary search-based resource allocation algorithm to balance editing latency across ESs. Extensive experiments demonstrate that CoKE and pCoKE reduce editing latency by 73% while maintaining high editing quality. Moreover, pCoKE achieves an approximately 6% improvement in editing quality.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2730-2745"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288509","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}

{"title":"Defense Against Membership Inference Attacks via Normalizing Flow-Based Adversarial Sample","authors":"Guangxu Xie;Yang Xiao;Qingqi Pei;Haibo Huang","doi":"10.1109/TSC.2026.3697251","DOIUrl":"10.1109/TSC.2026.3697251","url":null,"abstract":"An attacker can determine if a specific input belongs to a deep learning model’s training set by analyzing the complete confidence vector output, known as a membership inference attack. To counter this type of attack, a common defense strategy is to add adversarial noise to the confidence vector to enhance the model’s resilience. However, existing research indicates that defenses against adversarial examples may introduce new security risks to membership inference defenses via adversarial examples. Against this backdrop, this paper proposes a more robust adversarial example defense strategy. By generating adversarial examples with distributions similar to normal samples, we can reduce the impact of distributional differences on the defense model’s security. Leveraging normalizing flow, adding noise to the hidden layer of a flow model trained on normal samples generates adversarial examples that resemble normal ones, enhancing defense robustness. Additionally, limiting the information accessible to the attack model further strengthens robustness. To accelerate convergence, a bidirectional mean update method is employed. The proposed strategy demonstrates effectiveness not only in defending against membership inference attacks but also in mitigating attribute inference attacks. Finally, experiments on real-world datasets validate the effectiveness of the proposed strategy.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2776-2787"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288552","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}

{"title":"Efficient Microservice Autoscaling Through $mu$Opt","authors":"Emilio Incerto;Roberto Pizziol;Mirco Tribastone","doi":"10.1109/TSC.2026.3696863","DOIUrl":"10.1109/TSC.2026.3696863","url":null,"abstract":"Microservices have become the architecture of choice for cloud applications requiring high performance and scalability. Autoscaling, which dynamically adjusts resource allocation based on workload fluctuations, is key to optimizing performance and controlling costs. This paper presents <inline-formula><tex-math>$mu$</tex-math></inline-formula>Opt, a computationally efficient, model-based autoscaler specifically designed for microservices. <inline-formula><tex-math>$mu$</tex-math></inline-formula>Opt leverages a nonlinear optimization problem tied to a fluid approximation of a layered queuing network (LQN) model to determine optimal configurations that maximize key performance metrics—such as throughput, CPU usage, and response time—while minimizing operational costs. On a well-known benchmark application, our numerical experiments show that <inline-formula><tex-math>$mu$</tex-math></inline-formula>Opt achieves fast solution times, enabling responsiveness to dynamic workloads. Compared to a state-of-the-art LQN-based autoscaler employing genetic algorithms, <inline-formula><tex-math>$mu$</tex-math></inline-formula>Opt delivers improved application performance using fewer resources. To demonstrate the robustness and generalizability of our underlying model, we validate its prediction accuracy across ten randomly generated applications with diverse architectures, showing that its performance is a reliable foundation for autoscaling. Finally, it also outperforms Horizontal Pod Autoscaler, a production-ready solution for Kubernetes deployments in Google Cloud Platform, consistently reducing resource usage while more accurately tracking CPU utilization targets across both synthetic and real-world workloads.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2801-2814"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148290565","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}

{"title":"Leveraging Co-Occurrence Bias in Web API Recommendation via Causality-Inspired Context-Adjusted Graph Learning","authors":"Shengye Pang;Yihang Zhang;Song Yang;Yixin Chen;Yanglan Gan;Shuiguang Deng;Guobing Zou","doi":"10.1109/TSC.2026.3708601","DOIUrl":"10.1109/TSC.2026.3708601","url":null,"abstract":"Web API recommendation is crucial for mashup development, yet the rapid growth of service repositories causes severe information overload. Existing methods heavily exploit historical API co-occurrences, which can introduce spurious correlations bias across heterogeneous contexts and aggravate sparsity by ignoring valid but unobserved combinations. We propose CICA, a causality-inspired, context-adjusted graph learning framework that (i) disentangles functional semantics from contextual confounders and employs a Query-Aware Graph Neural Network to selectively activate context-relevant edges for dynamic noise filtering. Furthermore, the framework (ii) enriches sparse nodes with implicit semantic similarity edges to alleviate the sparsity bias. Finally, a causal adjustment mechanism is implemented to integrate functional matching with co-occurrence patterns by dynamically controlling the intervention of co-occurrence bias. Comprehensive experiments conducted on the real-world ProgrammableWeb dataset demonstrate that CICA consistently outperforms state-of-the-art baselines across standard evaluation metrics, including Precision, Recall, and NDCG.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2876-2888"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148371841","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}

{"title":"$mathsf {DARTIC}$: Decentralized Anonymous Reputation at Scale for Trustworthy Crowdsourcing","authors":"Mouhamed Amine Bouchiha;Mourad Rabah;Ronan Champagnat;Abdelaziz Amara Korba;Yacine Ghamri-Doudane","doi":"10.1109/TSC.2026.3710404","DOIUrl":"10.1109/TSC.2026.3710404","url":null,"abstract":"On-chain crowdsourcing leverages blockchain’s decentralization, transparency, and tamper-resistance to build trustworthy and verifiable Web3 crowdsourced services. However, existing decentralized reputation frameworks do not reconcile anonymity, reputation binding, and scalability. This paper demonstrates how on-chain crowdsourcing can simultaneously achieve these requirements under a trust-minimized model. We introduce <inline-formula><tex-math>$mathsf {DARTIC}$</tex-math></inline-formula>, a decentralized, anonymous, and scalable reputation-driven framework for crowdsourcing. <inline-formula><tex-math>$mathsf {DARTIC}$</tex-math></inline-formula> presents a dual-ledger system that enables requesters and workers to use distinct pseudonyms across interactions, ensuring unlinkability while maintaining accountability. To mitigate Sybil and reputation-reset attacks, we employ zkSNARK-based set membership proofs, cryptographically binding all user pseudonyms to a single access token without revealing the linkage. For scalability, we investigate two aggregation techniques that compress multiple proofs into a single succinct proof to minimize verification overhead. In addition, we design an automated, privacy-preserving reputation model that dynamically evaluates contributions across diverse crowdsourcing contexts. To demonstrate practicality, we instantiate and assess <inline-formula><tex-math>$mathsf {DARTIC}$</tex-math></inline-formula> in both crowdsensing and federated learning scenarios. Experimental results show that (i) individual proof generation for token spending completes in less than 3 s, (ii) aggregation reduces the verification time of 1024 proofs from 8.7 s to 0.96 s, and (iii) zk-batching lowers gas costs by more than 100× compared to a pure Layer-1 deployment. These results demonstrate that anonymity, robust reputation binding, and scalability can be jointly achieved in fully decentralized crowdsourcing systems.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2760-2775"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148401114","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}

{"title":"SLICE: SLO-Driven Scheduling for LLM Inference on Edge Computing Devices","authors":"Pan Zhou;Yiming Lei;Ling Liu;Xiaoqiong Xu;Hongfang Yu;Gang Sun;Daji Ergu;Yueyue Dai","doi":"10.1109/TSC.2026.3699152","DOIUrl":"10.1109/TSC.2026.3699152","url":null,"abstract":"Large Language Models (LLMs), as the foundational architecture for next-generation interactive AI applications, not only power intelligent dialogue systems but also drive the evolution of embodied intelligence on edge devices, including humanoid robots, smart vehicles, and other scenarios. The applications running on these edge devices impose differentiated Service Level Objectives (SLOs) on LLM services, specifying distinct constraints on time to first token (TTFT) and time per output token (TPOT) as well as end-to-end latency. Notably, edge devices typically handle real-time tasks that are extremely sensitive to latency, such as machine control and navigation planning. However, existing scheduling service systems still prioritize maximizing output token throughput as the sole optimization objective, failing to adequately address the diversity of SLO requirements. This ultimately results in persistently high violation rates for end-to-end latency or TPOT related SLOs. This paper proposes SLICE, an innovative scheduling solution designed for edge computing scenarios with differentiated SLO requirements. By combining a utility-maximizing request scheduling algorithm with a dynamic batching control mechanism for token generation, SLICE significantly improves the SLO attainment of LLM inference service. Experimental results demonstrate that compared to state-of-the-art solutions SLO-APMS and FastServe, SLICE achieves up to 35× higher SLO attainment and 2.9× advantage in task completion time than the other two solutions.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"3072-3084"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288605","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}

{"title":"STF: Steady and Transient Factorization for Sparse Time-Aware QoS Prediction","authors":"Yiji Zhao;Yunlong Gui;Lei Zhang;Jixian Zhang;Ming Jin;Hao Wu","doi":"10.1109/TSC.2026.3704220","DOIUrl":"10.1109/TSC.2026.3704220","url":null,"abstract":"Accurately predicting Quality of Service (QoS) under real-world sparsity is challenging because existing methods rarely distinguish between long-term steady trends and short-term transient perturbations, and often underutilize the collaborative filtering signals embedded in historical observations. To address these issues, we propose <italic>Steady and Transient Factorization (STF)</i>, a deep factorization framework that explicitly decomposes QoS into two interpretable components: a steady baseline capturing stable, long-term patterns, and a transient residual modeling short-term, interaction-specific variations. STF formulates time-aware QoS prediction as a multi-perspective learning problem, encompassing four complementary sub-tasks: user-wise, service-wise, user–service pair-wise, and user-service-time triplet-wise predictions, with dedicated steady and transient predictors applied to the first three. The outputs of these predictors are adaptively fused via a learnable weighting mechanism for end-to-end optimization, enabling the model to capture multi-level interaction granularity while enhancing robustness to sparsity. Extensive experiments on the large-scale dataset (30 million records) demonstrate that STF consistently outperforms state-of-the-art methods, reducing MAE and RMSE by up to 6.73% and 6.68% for response-time prediction, and reducing MAE and RMSE by up to 11.10% and 9.44% for throughput prediction, while maintaining competitive efficiency.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"3112-3125"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288507","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}

{"title":"$mathsf {SENTRY}$: A Compliance-Check Service for Dynamic Searchable Encryption With Sanitized Authorization Query","authors":"Jinhui Li;Lei Xu;Xiaoning Liu;Xun Yi;Ibrahim Khalil","doi":"10.1109/TSC.2026.3697760","DOIUrl":"10.1109/TSC.2026.3697760","url":null,"abstract":"Searchable encryption enables privacy-preserving queries over data outsourced to cloud services. Classical symmetric schemes deliver efficient search but largely assume a single-client setting; multi-client variants permit delegation yet typically treat authorization as an owner-local decision, overlooking regulations enforced by higher-level authorities (e.g., sector-specific compliance). In practice, limited familiarity with regulatory detail or operational lapses can lead owners to delegate search permissions that violate authority regulations, rendering existing systems unsuited to regulated, multi-client cloud environments. &lt;p&gt;To address this gap, we propose two systems. First, we propose &lt;inline-formula&gt;&lt;tex-math&gt;$mathsf {SENTRY}$&lt;/tex-math&gt;&lt;alternatives&gt;&lt;mml:math&gt;&lt;mml:mi&gt;SENTRY&lt;/mml:mi&gt;&lt;/mml:math&gt;&lt;inline-graphic&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;, a multi-client dynamic searchable encryption framework with a built-in &lt;i&gt;compliance-check service&lt;/i&gt; via &lt;i&gt;sanitized authorization&lt;/i&gt;. Its core is a tag-based sanitization protocol: the authority encodes prohibited keywords as hidden tags, and the sanitizer uses these tags to remove non-compliant per-keyword search permissions before they reach readers, without learning the underlying keywords. As a result, readers receive only compliant search capabilities. We then propose &lt;inline-formula&gt;&lt;tex-math&gt;$mathcal {F}-mathsf {SENTRY}$&lt;/tex-math&gt;&lt;alternatives&gt;&lt;mml:math&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;F&lt;/mml:mi&gt;&lt;mml:mo&gt;-&lt;/mml:mo&gt;&lt;mml:mi&gt;SENTRY&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:math&gt;&lt;inline-graphic&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;, a forward-private variant of &lt;inline-formula&gt;&lt;tex-math&gt;$mathsf {SENTRY}$&lt;/tex-math&gt;&lt;alternatives&gt;&lt;mml:math&gt;&lt;mml:mi&gt;SENTRY&lt;/mml:mi&gt;&lt;/mml:math&gt;&lt;inline-graphic&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; for settings where regulations evolve over time. &lt;inline-formula&gt;&lt;tex-math&gt;$mathcal {F}-mathsf {SENTRY}$&lt;/tex-math&gt;&lt;alternatives&gt;&lt;mml:math&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;F&lt;/mml:mi&gt;&lt;mml:mo&gt;-&lt;/mml:mo&gt;&lt;mml:mi&gt;SENTRY&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:math&gt;&lt;inline-graphic&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; preserves the same sanitized-authorization mechanism and further adds forward privacy through a tailored constrained shiftable encryption, which binds search permissions and newly added encrypted updates to regulatory epochs. Consequently, permissions issued before a policy change cannot be used to retrieve data added afterward unless they are refreshed for the new epoch. We formalize the security of both &lt;inline-formula&gt;&lt;tex-math&gt;$mathsf {SENTRY}$&lt;/tex-math&gt;&lt;alternatives&gt;&lt;mml:math&gt;&lt;mml:mi&gt;SENTRY&lt;/mml:mi&gt;&lt;/mml:math&gt;&lt;inline-graphic&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; and &lt;inline-formula&gt;&lt;tex-math&gt;$mathcal {F}-mathsf {SENTRY}$&lt;/tex-math&gt;&lt;alternatives&gt;&lt;mml:math&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;F&lt;/mml:mi&gt;&lt;mml:mo&gt;-&lt;/mml:mo&gt;&lt;mml:mi&gt;SENTRY&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:math&gt;&lt;inline-graphic&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; and prove them secure under standard assumptions. Experiments under cloud-like workloads show that &lt;inline-formula&gt;&lt;tex-math&gt;$mathsf {SENTRY}$&lt;/tex-","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"3042-3058"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288571","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}