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Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

Digital Communications and networks 2025年第2期11卷 473-481页

作者： Qi Li Gaozhan Liu Qianqian Zhang Lidong Han Wei Chen Rui Li Jinbo Xiong School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal UniversityHangzhou 311121China Tongda College of Nanjing University of Posts and Telecommunications Yangzhou 225127China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China School of Computer Science and Technology Xidian UniversityXi'an 710071China State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal UniversityXining 810008China Faculty of Software Fujian Normal UniversityFuzhou 350108China

Ciphertext-Policy Attribute-Based Encryption(CP-ABE)enables fine-grained access control on ciphertexts,making it a promising approach for managing data stored in the cloud-enabled Internet of *** existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute ***,resource-constrained IoT devices,especially those adopting wireless communication,frequently encounter affordability issues regarding decryption *** this paper,we propose an efficient and fine-grained access control scheme with fully hidden policies(named FHAC).FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them.A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access *** attacks are thwarted by providing unauthorized users with invalid *** heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud ***,users can verify the correctness of multiple outsourced decryption results *** analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.

关键词： Access control Policy hiding Verifiable outsourced computation Cloud IoT

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SQAT-LD: SPeech Quality Assessment Transformer Utilizing Listener Dependent Modeling for Zero-Shot Out-of-Domain MOS Prediction

SQAT-LD: SPeech Quality Assessment Transformer Utilizing Lis...

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2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Shen, Kailai Yan, Diqun Dong, Li Ren, Ying Wu, Xiaoxun Hu, Jing Ningbo University Faculty of Electrical Engineering and Computer Science China Zhejiang Key Laboratory of Mobile Network Application Technology China

ISBN: (纸本)9798350306897

In this paper, we propose the speech quality assessment transformer utilizing listener dependent modeling (SQAT-LD) mean opinion score (MOS) prediction system, which was submitted to the 2023 VoiceMOS Challenge. The system is based on a combination of self-supervised learning (SSL) models and listener-dependent modeling. Due to this challenge's emphasis on real-world and challenging zero-shot out-of-domain MOS prediction in three different voice evaluation scenarios, we specifically designed a two-branch module to predict scores and weights for each frame, aiming to achieve better generalization. In the challenge, our system achieved fourth place in Track 1a, second place in Track 1b and first place in Track 2. Additionally, we conducted an ablation study to investigate the effectiveness of our proposed method. © 2023 IEEE.

关键词： Forecasting

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An Efficient Anti-Quantum Blind Signature with Forward Security for Blockchain-Enabled Internet of Medical Things

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Computers, Materials & Continua 2025年第2期82卷 2293-2309页

作者： Gang Xu Xinyu Fan Xiu-Bo Chen Xin Liu Zongpeng Li Yanhui Mao Kejia Zhang School of Information Science and Technology North China University of TechnologyBeijing100144China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China School of Mathematical Science Heilongjiang UniversityHarbin150080China School of Digtial and Intelligence Industry Inner Mongolia University of Science and TechnologyBaotou014010China Institute for Network Sciences and Cyberspace Tsinghua UniversityBeijing100084China Yunnan Key Laboratory of Blockchain Application Technology Kunming650233China Yunnan Innovation Institute of Beihang University Kunming650233China

Blockchain-enabled Internet of Medical Things (BIoMT) has attracted significant attention from academia and healthcare organizations. However, the large amount of medical data involved in BIoMT has also raised concerns about data security and personal privacy protection. To alleviate these concerns, blind signature technology has emerged as an effective method to solve blindness and unforgeability. Unfortunately, most existing blind signature schemes suffer from the security risk of key leakage. In addition, traditional blind signature schemes are also vulnerable to quantum computing attacks. Therefore, it remains a crucial and ongoing challenge to explore the construction of key-secure, quantum-resistant blind signatures. In this paper, we introduce lattice-based forward-secure blind signature (LFSBS), a lattice-based forward-secure blind signature scheme for medical privacy preservation in BIoMT. LFSBS achieves forward security by constructing a key evolution mechanism using a binary tree structure. This mechanism ensures that even if future encryption keys are leaked, past data can still remain secure. Meanwhile, LFSBS realizes post-quantum security based on the hardness assumption of small integer solution (SIS), making it resistant to potential quantum computing attacks. In addition, we formally define and prove the security of LFSBS in a random oracle model, including blindness and forward-secure unforgeability. Comprehensive performance evaluation shows that LFSBS performs well in terms of computational overhead, with a reduction of 22%–73% compared to previous schemes.

关键词： Internet of Things blockchain forward-secure blind signature

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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HVAC Optimal Control Based on the Sensitivity Analysis:An Improved SA Combination Method Based on a Neural network

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2741-2758页

作者： Lifan Zhao Zetian Huang Qiming Fu Nengwei Fang Bin Xing Jianping Chen School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China Suzhou Key Laboratory of Mobile Network Technology and Application Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

Aiming at optimizing the energy consumption of HVAC,an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis(SA),named the sensitivity analysis combination method(SAC).Based on the SA,neural network and the related settings about energy conservation of HVAC systems,such as cooling water temperature,chilled water temperature and supply air temperature,were ***,based on the data of the existing HVAC system,various optimal control methods ofHVAC systems were tested and evaluated by a simulated HVAC system in *** results show that the proposed SA combination method can reduce significant computational load while maintaining an equivalent energy performance compared with traditional optimal control methods.

关键词： Energy conservation sensitivity analysis HVAC system neural network

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Fast Crop Pest Detection Using Lightweight Feature Extraction and Knowledge Distillation

Fast Crop Pest Detection Using Lightweight Feature Extractio...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Yang, Ze Jiang, Xianliang Jin, Guang Huang, Junkai Bai, Jie Yu, Dingxin Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

ISBN: (纸本)9798350337020

Pest detection is critical for achieving effective pest control. However, the current deep learning-based pest detection algorithm is unsuitable for deployment on resource-limited edge devices due to its extensive computation and long inference time. Although lightweight models have been widely used for practical detection, their insufficient feature extraction capability leads to a decline in detection accuracy. This paper proposes a fast algorithm for crop pest detection based on lightweight feature extraction and knowledge distillation. Firstly, we introduce partial convolution and propose a lightweight feature extraction module, C3Faster, which reduces the model's computation and speeds up model inference while ensuring effective feature extraction. Secondly, we use knowledge distillation to improve the model's detection accuracy by using teacher networks to assist in training. Finally, we created a dataset, CropPest6, consisting of six crop pest categories and conducted experiments. The experimental results demonstrate that our method reduces the detection time, number of parameters, and computation by 17%, 38%, and 44%, respectively, compared to the baseline model. Furthermore, our method achieves 93.9% Precision, 93.6% Recall, and 97.5% mean Average Precision (mAP), demonstrating its practical suitability for fast crop pest detection. © 2023 IEEE.

关键词： crop pests deep learning object detection pest detection

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A Multi Path Abnormal Data Filtering Algorithm for Space-Air-Ground Integrated networks 4

A Multi Path Abnormal Data Filtering Algorithm for Space-Air...

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4th International Conference on Neural networks, Information and Communication Engineering, NNICE 2024

作者： Huang, Rui Li, Jingyi Li, Bingjie Li, Guo Cao, Suzhi Xu, Xiaobin China Satellite Network Application Co. Ltd Beijing China China Satellite Network Application Co. Ltd Operations Services Department Beijing China Beijing University of Technology Faculty of Information Technology Beijing China Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Cas Beijing China

ISBN: (纸本)9798350394375

Space-Air-Ground Integrated networks (SAGINs) are important infrastructures of 6G. With the support of SAGINs, data collection in unattended areas becomes possible. However, data measured by various devices in unattended areas is often unreliable. Original collected data are usually accompanied with noise, error, or inconsistent value. To ensure the accuracy and efficiency of urban computing, real-time and light-weight outlier filtering becomes a prerequisite of urban applications. In practical SAGIN applications, software defined wireless networking provides the capable of managing the whole wireless network globally, while edge computing enables the network edge of SAGIN hold the computation capacity. Based on the combination of these two technologies, a real-time multi path abnormal data filtering algorithm is proposed. In the proposed algorithm, two abnormal data filters are utilized, a hierarchical distributed scheme is performed in the edge server to acquire low overhead and real-time requirements. The performance of the proposed outlier filtering algorithm is validated in the simulations. © 2024 IEEE.

关键词： application programs

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Secondary Access Class Barring for Machine Type Communications with PUSCH Resource Constraints 100

Secondary Access Class Barring for Machine Type Communicatio...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Wen, Yuxuan Yang, Liu Bai, Chaoyuan Liu, Heng Chengdu611756 China Southwest Jiaotong University Key Lab of Information Coding & Transmission Institude of Mobile Communication Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China

ISBN: (纸本)9798331517786

With the continuous increase of IoT devices, multiple devices desire to use network resources simultaneously during peak hours and overload conditions, leading to collisions and a reduction in the network's effective throughput. The Access Class Barring (ACB) method has been proposed as the optimal potential solution in LTE communication networks to overcome this problem. However, existing ACB schemes do not fully utilize preamble resources, and most schemes do not consider the situation where PUSCH resources are limited. Therefore, this paper proposes a two-stage ACB algorithm that considers the situation of limited PUSCH resources. This algorithm recycles part of the preamble resources and sets the ACB factor based on the number of PUSCH resources. Simulation results show that our proposed algorithm significantly outperforms traditional schemes in terms of total service time, delay, throughput, and PUSCH resource efficiency. © 2024 IEEE.

关键词： Internet of things

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Improved Sparse Activity Detection Based on Optimized Measurement Matrix in mMTC 100

Improved Sparse Activity Detection Based on Optimized Measur...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Wang, Jianyu Yang, Liu Bai, Chaoyuan Liu, Heng Chengdu611756 China Southwest Jiaotong University Key Lab of Information Coding & Transmission Institude of Mobile Communication Chengdu611756 China Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China

ISBN: (纸本)9798331517786

Device activity detection and channel estimation are central problems in massive Machine Type Communication (mMTC) scenarios. Given the sparse distribution of active devices, compressive sensing (CS) emerges as a viable solution at the gNB to mitigate interference stemming from non-orthogonal preamble sets. This paper proposes an algorithm for optimizing measurement matrix, thereby elevating the performance of signal recovery in CS. Within the proposed algorithm, a optimization model that integrates eigenvalue averaging and equiangular tight frame (ETF) theory is formulated, and an iterative algorithm is devised to approximate the optimal solution utilizing singular value decomposition (SVD). Further, a weight-based strategie is discussed with respect to the feasibility of the proposed algorithm, accompanied by the introduction of iteration termination condition. Experimental results corroborate the algorithm's swift convergence, underpinning the efficient production of high-performance matrices that refine the accuracy of joint device activity detection and channel estimation. © 2024 IEEE.

关键词： Eigenvalues and eigenfunctions

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Advancing Few-Shot Instance Segmentation of Military Aircraft in Remote Sensing Imagery with Large Vision Models

Advancing Few-Shot Instance Segmentation of Military Aircraf...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xiyu Qi Yifan Wu Wenhui Zhang Zhan Chen Yidan Zhang Lei Wang Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Target Cognition and Application Technology (TCAT) Key Laboratory of Network Information System Technology (NIST) Key Laboratory of Technology in Geo-Spatial Information Processing and Application System

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This study introduces a novel few-shot instance segmentation framework MAR FSIS for identifying and segmenting aircraft in high-resolution remote sensing imagery under stringent few-shot conditions. Utilizing large vision models in conjunction with a sophisticated detector and a novel object prompter, MAR FSIS demonstrates superior performance in discerning intricate details of aircraft with minimal training examples. The effectiveness of this approach is confirmed through experimental evaluations on the proposed MAR20IS dataset, achieving remarkable precision in both detection and segmentation tasks.

关键词： Instance segmentation Training Atmospheric modeling Detectors Military aircraft Data models Proposals

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