IEEE Transactions on Mobile Computing最新文献_第2页

Lightweight Configuration Adaptation With Multi-Teacher Reinforcement Learning for Live Video Analytics 轻量级配置适应与多教师强化学习实时视频分析

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-06 DOI: 10.1109/TMC.2025.3526359

Yuanhong Zhang;Weizhan Zhang;Muyao Yuan;Liang Xu;Caixia Yan;Tieliang Gong;Haipeng Du

{"title":"Lightweight Configuration Adaptation With Multi-Teacher Reinforcement Learning for Live Video Analytics","authors":"Yuanhong Zhang;Weizhan Zhang;Muyao Yuan;Liang Xu;Caixia Yan;Tieliang Gong;Haipeng Du","doi":"10.1109/TMC.2025.3526359","DOIUrl":"https://doi.org/10.1109/TMC.2025.3526359","url":null,"abstract":"The proliferation of video data and advancements in Deep Neural Networks (DNNs) have greatly boosted live video analytics, driven by the growing video capture capabilities of mobile devices. However, resource limitations necessitate the transmission of endpoint-collected videos to servers for inference. To meet real-time requirements and ensure accurate inference, it is essential to adjust video configurations at the endpoint. Traditional methods rely on deterministic strategies, posing difficulties in adapting to dynamic networks and video content. Meanwhile, emerging learning-based schemes suffer from trial-and-error exploration mechanisms, resulting in a concerning long-tail effect on upload latency. In this paper, we propose a novel lightweight and robust configuration adaptation policy (LCA), which fuses heuristic and RL-based agents using multi-teacher knowledge distillation (MKD) theory. First, we propose a content-sensitive and bandwidth-adaptive RL agent and introduce a Lyapunov-based optimization agent for ensuring latency robustness. To leverage both agents’ strengths, we design a feature-guided multi-teacher distillation network to transfer their advantages to the student. The experimental results across two vision tasks (pose estimation and semantic segmentation) demonstrate that LCA significantly reduces transmission latency compared to prior work (average reduction of 47.11%-89.55%, 95-percentile reduction of 27.63%-88.78%) and computational overhead while maintaining comparable inference accuracy.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4466-4480"},"PeriodicalIF":7.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786354","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

HBF MU-MIMO With Interference-Aware Beam Pair Link Allocation for Beyond-5G mm-Wave Networks 超5g毫米波网络中具有干扰感知的HBF MU-MIMO波束对链路分配

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-06 DOI: 10.1109/TMC.2025.3526547

Aleksandar Ichkov;Alexander Wietfeld;Marina Petrova;Ljiljana Simić

{"title":"HBF MU-MIMO With Interference-Aware Beam Pair Link Allocation for Beyond-5G mm-Wave Networks","authors":"Aleksandar Ichkov;Alexander Wietfeld;Marina Petrova;Ljiljana Simić","doi":"10.1109/TMC.2025.3526547","DOIUrl":"https://doi.org/10.1109/TMC.2025.3526547","url":null,"abstract":"Hybrid beamforming (HBF) multi-user multiple-input multiple-output (MU-MIMO) is a key technology for unlocking the directional millimeter-wave (mm-wave) nature for spatial multiplexing beyond current codebook-based 5G-NR networks. In order to suppress co-scheduled users’ interference, HBF MU-MIMO is predicated on having sufficient radio frequency chains and accurate channel state information (CSI), which can otherwise lead to performance losses due to imperfect interference cancellation. In this work, we propose <italic>IABA, a 5G-NR standard-compliant beam pair link (BPL) allocation scheme for mitigating spatial interference in practical HBF MU-MIMO networks. <italic>IABA solves the network sum throughput optimization via either a <italic>distributed or a <italic>centralized BPL allocation using dedicated CSI reference signals for candidate BPL monitoring. We present a comprehensive study of practical multi-cell mm-wave networks and demonstrate that HBF MU-MIMO without interference-aware BPL allocation experiences strong residual interference which limits the achievable network performance. Our results show that <italic>IABA offers significant performance gains over the default interference-agnostic 5G-NR BPL allocation, and even allows HBF MU-MIMO to outperform the fully digital MU-MIMO baseline, by facilitating allocation of secondary BPLs other than the strongest BPL found during initial access. We further demonstrate the scalability of <italic>IABA with increased gNB antennas and densification for beyond-5G mm-wave networks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4248-4262"},"PeriodicalIF":7.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783237","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

Enhancing Remote Sensing Image Scene Classification With Satellite-Terrestrial Collaboration and Attention-Aware Transmission Policy 基于星地协同和注意感知传输策略的遥感影像场景分类

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-06 DOI: 10.1109/TMC.2025.3526142

Anqi Lu;Youbing Hu;Zhiqiang Cao;Jie Liu;Lingzhi Li;Zhijun Li

{"title":"Enhancing Remote Sensing Image Scene Classification With Satellite-Terrestrial Collaboration and Attention-Aware Transmission Policy","authors":"Anqi Lu;Youbing Hu;Zhiqiang Cao;Jie Liu;Lingzhi Li;Zhijun Li","doi":"10.1109/TMC.2025.3526142","DOIUrl":"https://doi.org/10.1109/TMC.2025.3526142","url":null,"abstract":"Advancements in Earth observation sensors on low Earth orbit (LEO) satellites have significantly increased the volume of remote sensing images. This growth has led to challenges such as higher storage demands, downlink bandwidth stress, and transmission delays, particularly for real-time remote sensing image scene classification (RSISC). To address this, we propose a novel Satellite-Terrestrial Collaborative Scene Classification (STCSC) framework that integrates transmission and computation. The framework employs an attention-aware policy on the satellite, which adaptively determines the sequence of images and selection of image blocks for transmission, as well as these blocks’ sampling rates. This policy is based on image complexity and the real-time data transmission rate, prioritizing blocks crucial for downstream tasks. On the ground, a classification model processes the received image blocks, balancing classification accuracy and transmission delay. Moreover, we have developed a comprehensive simulation system to validate the performance of our framework, including simulations of the satellite, transmission, and ground modules. Simulation results demonstrate that our STCSC framework can reduce transmission delay by 76.6% while enhancing classification accuracy on the ground by 0.6%. Additionally, our attention-aware policy is compatible with any ground classification model.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4496-4509"},"PeriodicalIF":7.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786349","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

An Improved Ultra-Lightweight Anonymous Authenticated Key Agreement Protocol for Wearable Devices 一种改进的可穿戴设备超轻量匿名认证密钥协议

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-06 DOI: 10.1109/TMC.2025.3526076

Xin Ai;Akhtar Badshah;Shanshan Tu;Muhammad Waqas;Iftekhar Ahmad

{"title":"An Improved Ultra-Lightweight Anonymous Authenticated Key Agreement Protocol for Wearable Devices","authors":"Xin Ai;Akhtar Badshah;Shanshan Tu;Muhammad Waqas;Iftekhar Ahmad","doi":"10.1109/TMC.2025.3526076","DOIUrl":"https://doi.org/10.1109/TMC.2025.3526076","url":null,"abstract":"For wearable devices with constrained computational resources, it is typically required to offload processing tasks to more capable servers. However, this practice introduces vulnerabilities to data confidentiality and integrity due to potential malicious network attacks, unreliable servers, and insecure communication channels. A robust mechanism that ensures anonymous authentication and key agreement is therefore imperative for safeguarding the authenticity of computing entities and securing data during transmission. Recently, Guo et al. proposed an anonymous authentication key agreement and group proof protocol specifically designed for wearable devices. This protocol, benefiting from the strengths of previous research, is designed to thwart a variety of cyber threats. However, inaccuracies in their protocol lead to issues with authenticity verification, ultimately preventing the establishment of secure session keys between communication entities. To address these design flaws, an improved ultra-lightweight protocol was proposed, employing cryptographic hash functions to ensure authentication and privacy during data transmission in wearable devices. Supported by rigorous security validations and analyses, the proposed protocol significantly boosts both security and efficiency, marking a substantial advancement over prior methodologies.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4543-4557"},"PeriodicalIF":7.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786332","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

Generative Diffusion-Based Contract Design for Efficient AI Twin Migration in Vehicular Embodied AI Networks 基于生成扩散的车载人工智能网络双元高效迁移契约设计

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-06 DOI: 10.1109/TMC.2025.3526230

Yue Zhong;Jiawen Kang;Jinbo Wen;Dongdong Ye;Jiangtian Nie;Dusit Niyato;Xiaozheng Gao;Shengli Xie

{"title":"Generative Diffusion-Based Contract Design for Efficient AI Twin Migration in Vehicular Embodied AI Networks","authors":"Yue Zhong;Jiawen Kang;Jinbo Wen;Dongdong Ye;Jiangtian Nie;Dusit Niyato;Xiaozheng Gao;Shengli Xie","doi":"10.1109/TMC.2025.3526230","DOIUrl":"https://doi.org/10.1109/TMC.2025.3526230","url":null,"abstract":"Embodied Artificial Intelligence (AI) bridges the cyberspace and the physical space, driving advancements in autonomous systems like the <underline>Vehicular <underline>Embodied <underline>AI <underline>NETwork (VEANET). VEANET integrates advanced AI capabilities into vehicular systems to enhance autonomous operations and decision-making. Embodied agents, such as Autonomous Vehicles (AVs), are autonomous entities that can perceive their environment and take actions to achieve specific goals, actively interacting with the physical world. Embodied Agent Twins (EATs) are digital models of these embodied agents, with various Embodied Agent AI Twins (EAATs) for intelligent applications in cyberspace. In VEANETs, EAATs act as in-vehicle AI assistants to perform diverse tasks supporting autonomous driving using generative AI models. Due to limited onboard computational resources, AVs offload EAATs to nearby RoadSide Units (RSUs). However, the mobility of AVs and limited RSU coverage necessitates dynamic migrations of EAATs, posing challenges in selecting suitable RSUs under information asymmetry. To address this, we construct a multi-dimensional contract theoretical model between AVs and alternative RSUs. Considering that AVs may exhibit irrational behavior, we utilize prospect theory instead of expected utility theory to model the actual utilities of AVs. Finally, we employ a Generative Diffusion Model (GDM)-based algorithm to identify the optimal contract designs, thus enhancing the efficiency of EAAT migrations. Numerical results demonstrate the superior efficiency of the proposed GDM-based scheme in facilitating EAAT migrations compared with traditional deep reinforcement learning methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4573-4588"},"PeriodicalIF":7.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786331","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

Bi-DeepViT: Binarized Transformer for Efficient Sensor-Based Human Activity Recognition 基于传感器的高效人体活动识别的二值化变压器

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-06 DOI: 10.1109/TMC.2025.3526166

Fei Luo;Anna Li;Salabat Khan;Kaishun Wu;Lu Wang

{"title":"Bi-DeepViT: Binarized Transformer for Efficient Sensor-Based Human Activity Recognition","authors":"Fei Luo;Anna Li;Salabat Khan;Kaishun Wu;Lu Wang","doi":"10.1109/TMC.2025.3526166","DOIUrl":"https://doi.org/10.1109/TMC.2025.3526166","url":null,"abstract":"Transformer architectures are popularized in both vision and natural language processing tasks, and they have achieved new performance benchmarks because of their long-term dependencies modeling, efficient parallel processing, and increased model capacity. While transformers offer powerful capabilities, their demanding computational requirements clash with the real-time and energy-efficient needs of edge-oriented human activity recognition. It is necessary to compress the transformer to reduce its memory consumption and accelerate the inference. In this paper, we investigated the binarization of a transformer-DeepViT for efficient human activity recognition. For feeding sensor signals into DeepViT, we first processed sensor signals to spectrograms by using wavelet transform. Then we applied three methods to binarize DeepViT and evaluated it on three public benchmark datasets for sensor-based human activity recognition. Compared to the full-precision DeepViT, the fully binarized one (Bi-DeepViT) reduced about 96.7% model size and 99% BOPs (Bit Operations) with only a little accuracy compromised. Furthermore, we explored the effects of binarizing various components and latent binarization of DeepViT to understand their impact on the model. We also validated the performance of Bi-DeepViTs on two wireless sensing datasets. The result shows that a certain partial binarization can improve the performance of DeepViT. Our work is the first to apply a binarized transformer in HAR.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4419-4433"},"PeriodicalIF":7.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786350","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

Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground Truth? 没有地面真实，能否提升移动众测数据质量？

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-06 DOI: 10.1109/TMC.2025.3526277

Jiajie Li;Bo Gu;Shimin Gong;Zhou Su;Mohsen Guizani

{"title":"Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground Truth?","authors":"Jiajie Li;Bo Gu;Shimin Gong;Zhou Su;Mohsen Guizani","doi":"10.1109/TMC.2025.3526277","DOIUrl":"https://doi.org/10.1109/TMC.2025.3526277","url":null,"abstract":"Mobile crowdsensing (MCS) has emerged as a prominent trend across various domains. However, ensuring the quality of the sensing data submitted by mobile users (MUs) remains a complex and challenging problem. To address this challenge, an advanced method is needed to detect low-quality sensing data and identify malicious MUs that may disrupt the normal operations of an MCS system. Therefore, this article proposes a prediction- and reputation-based truth discovery (PRBTD) framework, which can separate low-quality data from high-quality data in sensing tasks. First, we apply a correlation-focused spatio-temporal Transformer network that learns from the historical sensing data and predicts the ground truth of the data submitted by MUs. However, due to the noise in historical data for training and the bursty values within sensing data, the prediction results can be inaccurate. To address this issue, we use the implications among the sensing data, which are learned from the prediction results but are stable and less affected by inaccurate predictions, to evaluate the quality of the data. Finally, we design a reputation-based truth discovery (TD) module for identifying low-quality data with their implications. Given the sensing data submitted by MUs, PRBTD can eliminate the data with heavy noise and identify malicious MUs with high accuracy. Extensive experimental results demonstrate that the PRBTD method outperforms existing methods in terms of identification accuracy and data quality enhancement.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4451-4465"},"PeriodicalIF":7.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786351","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-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction 基于图注意特征关联提取的多任务语义通信

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-03 DOI: 10.1109/TMC.2025.3525477

Xi Yu;Tiejun Lv;Weicai Li;Wei Ni;Dusit Niyato;Ekram Hossain

{"title":"Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction","authors":"Xi Yu;Tiejun Lv;Weicai Li;Wei Ni;Dusit Niyato;Ekram Hossain","doi":"10.1109/TMC.2025.3525477","DOIUrl":"https://doi.org/10.1109/TMC.2025.3525477","url":null,"abstract":"Multi-task semantic communication can serve multiple learning tasks using a shared encoder model. Existing models have overlooked the intricate relationships between features extracted during an encoding process of tasks. This paper presents a new graph attention inter-block (GAI) module to the encoder/ transmitter of a multi-task semantic communication system, which enriches the features for multiple tasks by embedding the intermediate outputs of encoding in the features, compared to the existing techniques. The key idea is that we interpret the outputs of the intermediate feature extraction blocks of the encoder as the nodes of a graph to capture the correlations of the intermediate features. Another important aspect is that we refine the node representation using a graph attention mechanism to extract the correlations and a multi-layer perceptron network to associate the node representations with different tasks. Consequently, the intermediate features are weighted and embedded into the features transmitted for executing multiple tasks at the receiver. Experiments demonstrate that the proposed model surpasses the most competitive and publicly available models by 11.4% on the CityScapes 2Task dataset and outperforms the established state-of-the-art by 3.97% on the NYU V2 3Task dataset, respectively, when the bandwidth ratio of the communication channel (i.e., compression level for transmission over the channel) is as constrained as <inline-formula><tex-math>$frac{1}{12}$</tex-math></inline-formula>.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4371-4388"},"PeriodicalIF":7.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786383","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

Alleviating Data Sparsity to Enhance AI Models Robustness in IoT Network Security Context 缓解数据稀疏性增强物联网网络安全环境下人工智能模型的鲁棒性

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-03 DOI: 10.1109/TMC.2025.3525463

Keshav Sood;Shigang Liu;Dinh Duc Nha Nguyen;Neeraj Kumar;Bohao Feng;Shui Yu

{"title":"Alleviating Data Sparsity to Enhance AI Models Robustness in IoT Network Security Context","authors":"Keshav Sood;Shigang Liu;Dinh Duc Nha Nguyen;Neeraj Kumar;Bohao Feng;Shui Yu","doi":"10.1109/TMC.2025.3525463","DOIUrl":"https://doi.org/10.1109/TMC.2025.3525463","url":null,"abstract":"In Internet of Things (IoT) networks, the IoT sensors collect valuable raw data required to sustain Artificial Intelligence (AI) based networks operation. AI models are data-driven as they use the data to make accurate network security, management, and operational decisions. Unfortunately, the sensors are deployed in harsh environments which affects the sensor behaviour and eventually the networks’ operations. Further, IoT devices are typically vulnerable to a range of malicious events. Therefore, IoT sensor's correct operation including resilience to failure is essential for sustained operations. Naturally, the state variables of time-series data can be changed, i.e., the data streams generated in these situations can be incorrect, incomplete or missing, and sparse presenting a significant challenge for real-time decision-making ability of AI models to make explainable and intelligent management and control decisions. In this paper, we aim to alleviate this fundamental problem to predict the missing and faulty reading correctly so that the decision-making ability of the AI models should not deteriorate in the presence of incorrect, missing, and highly imbalanced data sets. We use a novel approach using fuzzy-based information decomposition to recover the missed data values. We use three data sets, and our preliminary results show that our approach effectively recovers the missed or compromised data samples and help AI models in making accurate decision. Finally, the limitations and future work of this research have been discussed.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3764-3778"},"PeriodicalIF":7.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776239","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

AugSSO: Secure Threshold Single-Sign-On Authentication With Popular Password Collection AugSSO：安全阈值单点登录认证与流行的密码收集

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-03 DOI: 10.1109/TMC.2024.3525453

Changsong Jiang;Chunxiang Xu;Guomin Yang

{"title":"AugSSO: Secure Threshold Single-Sign-On Authentication With Popular Password Collection","authors":"Changsong Jiang;Chunxiang Xu;Guomin Yang","doi":"10.1109/TMC.2024.3525453","DOIUrl":"https://doi.org/10.1109/TMC.2024.3525453","url":null,"abstract":"Single-sign-on authentication is widely deployed in mobile systems, which allows an identity server to authenticate a mobile user and issue her/him with a token, such that the user can access diverse mobile services. To address the single-point-of-failure problem, threshold single-sign-on authentication (PbTA) is a feasible solution, where multiple identity servers perform user authentication and token issuance in a threshold way. However, existing PbTA schemes confront critical drawbacks. Specifically, these schemes are vulnerable to perpetual secret leakage attacks (PSLA): an adversary perpetually compromises secrets of identity servers (e.g., secret key shares or credentials) to break security. Besides, they fail to achieve popular password collection, which is an effective means of enhancing system security. In this paper, we propose a secure PbTA scheme with popular password collection, dubbed AugSSO. In AugSSO, we conceive an efficient key renewal mechanism that allows identity servers to periodically update secret key shares in batches, and require storage of hardened password-derived public keys in credentials for user authentication, thereby resisting PSLA. We also present a popular password collection mechanism, where an aggregation server is introduced to identify popular passwords without disclosing unpopular ones. We provide security analysis and performance evaluation to demonstrate security and efficiency of AugSSO.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4355-4370"},"PeriodicalIF":7.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786336","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