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Verifiable searchable symmetric encryption for conjunctive keyword queries in cloud storage

Frontiers of computer Science 2022年第6期16卷 103-121页

作者： Qingqing GAN Joseph K.LIU Xiaoming WANG Xingliang YUAN Shi-Feng SUN Daxin HUANG Cong ZUO Jianfeng WANG Guangzhou Key Laboratory of Multilingual Intelligent Processing School of Information Science and Technology/School of Cyber SecurityGuangdong University of Foreign StudiesGuangzhou510006China Department of Computer Science Jinan UniversityGuangzhou510632China Faculty of Information Technology Monash UniversityClayton3168Australia State Key Laboratory of Integrated Service Networks(ISN) Xidian UniversityXi’an710071China

Searchable symmetric encryption(SSE)has been introduced for secure outsourcing the encrypted database to cloud storage,while maintaining searchable *** various SSE schemes,most of them assume the server is honest but curious,while the server may be trustless in the real *** a malicious server not honestly performing the queries,verifiable SSE(VSSE)schemes are constructed to ensure the verifiability of the search ***,existing VSSE constructions only focus on single-keyword search or incur heavy computational cost during *** address this challenge,we present an efficient VSSE scheme,built on OXT protocol(Cash et al.,CRYPTO 2013),for conjunctive keyword queries with sublinear search *** proposed VSSE scheme is based on a privacy-preserving hash-based accumulator,by leveraging a well-established cryptographic primitive,Symmetric Hidden Vector Encryption(SHVE).Our VSSE scheme enables both correctness and completeness verifiability for the result without pairing operations,thus greatly reducing the computational cost in the verification ***,the proposed VSSE scheme can still provide a proof when the search result is ***,the security analysis and experimental evaluation are given to demonstrate the security and practicality of the proposed scheme.

关键词： searchable symmetric encryption verifiability conjunctive keyword queries hash-based accumulator cloud storage

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Civil Engineering Design in IoT: Leveraging Improved Swarm Intelligence Optimization 17

Civil Engineering Design in IoT: Leveraging Improved Swarm I...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Fu, Weiheng Chen, Rongzhong Chen, Kaiwen Xie, Xiaojun Fuzhou University College of Computer and Data Science Fuzhou China Xiamen Meteorological Bureau Xiamen Key Laboratory of Straits Meteorology Xiamen China Florida Transportarion Engineeeing Inc Roadway Design Department Punta Gorda United States Fuzhou University Zhicheng College Office of Network Security and Information Fuzhou China

ISBN: (纸本)9798350351637

Nowadays, swarm intelligence algorithms are used to solve various problem in IoT environments because of their excellent performance, and the particle swarm algorithm(PSO) is a superior algorithm in swarm intelligence algorithms. The original PSO, however, exhibits certain limitations. For instance, the utilization of random initialization results in a less diverse population distribution for PSO. During the algorithm iteration process, excessively large or small weight factors can also detrimentally impact the performance of the algorithm. Moreover, when dealing with multi-modal problems, the PSO algorithm can quickly converge to local optimal because of its "precocious". In this paper, a Double-Population Particle Swarm Optimization(DP-PSO) algorithm is proposed. Firstly, the chaotic mechanism is used to initialize the population to make its distribution more uniform and obtain better convergence results. Secondly, the oscillation characteristic of the sine function is used to improve the updating method of weight by adding the sine factor, and the search area of the particle is expanded. Finally, the double-population variation mechanism is introduced to enhance the variation ability and improve the solving accuracy in the later iteration. The experimental results show that the DP-PSO is superior to other algorithms in performance and has obvious improvement in the ability to expand the search space of the population and jump out of the local optimal solution. When applied to cantilever beam design, DP-PSO also has good competitiveness in terms of stability, robustness, and scalability. © 2024 IEEE.

关键词： Swarm intelligence

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OdCFU-Net for Brain Tumor Segmentation Based on MRI

OdCFU-Net for Brain Tumor Segmentation Based on MRI

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2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2024

作者： Wu, Shang Yang, Sijing Li, Boyue Yin, Shimin Zhu, Jianming Li, Shiyong Chen, Zhencheng Guilin University of Electronic Science and Technology School of Computer and Information Security China School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin China Key Laboratory of Biomedical Sensors and Intelligent Instruments Guangxi Colleges and Universities Guilin China Guangxi Engineering Technology Research Center of Human-Physiological Information Noninvasive Detection Guilin China School of Computer and Information Security Guilin University of Electronic Science and Technology China

ISBN: (纸本)9798350322996

In the field of medical image analysis, the accurate segmentation of brain tumors plays a crucial role in the diagnosis and treatment of brain-related diseases. This study aims to address key challenges in this domain, with a particular focus on improving feature extraction, information integration, and model generalization capabilities. By concurrently introducing multi-dimensional attention mechanisms in convolutional operations, a method has been developed to dynamically generate convolution kernels in spatial, input channel, and output channel dimensions. This approach optimizes the limitations of traditional static convolutional kernels, maintaining high-performance efficiency while reducing the model's parameter count. To overcome the deficiencies of traditional convolutional networks in capturing global information, channel and spatial attention mechanisms have been integrated between convolutional layers. This enhances adaptability and global information retrieval capabilities, allowing the model to dynamically adjust the weight of feature maps based on different task requirements. Leveraging the advantages of Vision Transformer in handling global information and addressing its limitations in capturing details and training on small datasets, the algorithm aligns and learns feature representations effectively by incorporating channel attention and normalizing attention weights within its multi-head attention. When applied to the BraTs2021 multi-modal brain tumor image database, the proposed algorithm accurately delineates brain tumor regions, demonstrating excellent performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Edge-Based Resilient Consensus Control of Islanded Microgrids 1

Edge-Based Resilient Consensus Control of Islanded Microgrid...

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1st International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2022

作者： Jiang, Yixin Xu, Peiming Xu, Wenqian Liang, Zhihong Yang, Yiwei Zhang, Yunan Huang, Kaitian Zhai, Meina Electric Power Research Institute Csg Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou China Northeastern University College of Information Science and Engineering Shenyang110819 China

ISBN: (纸本)9781665452151

We present a resilient output voltage recovery strategy for AC microgrids (MGs). In the presence of network attacks, distributed generators (DGs) may experience partial loss of effectiveness (PLOE) and bias faults in their actuators. These fault parameters can be heterogeneous and time-varying, posing significant challenges to voltage regulation. To address this, our approach incorporates adaptive techniques to handle the fault parameters while avoiding their direct dependence. Furthermore, we utilize boundary layers to design smooth controllers that mitigate the adverse effects of non-smooth controllers, ensuring stable and precise voltage regulation. Through extensive simulations, we validate the effectiveness of our proposed strategy in achieving reliable and robust load voltage regulation in AC MGs. © 2023 IEEE.

关键词： Multi agent systems

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Visible-Infrared Person Search: A Novel Benchmark and Solution 27th

Visible-Infrared Person Search: A Novel Benchmark and Solut...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Chen, Hongxu Xiong, Jianghao Huang, Yuheng Xie, Xiaohua Lai, Jian-Huang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Guangzhou GRGBanking Equipment Co. Ltd. Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9783031783401

Person search aims to simultaneously localize and identify a query person from realistic and uncropped images, which consists of person detection and re-identification (Re-ID). Existing person search methods and datasets predominantly focus on the visible light domain, and have difficulty in alleviating modality discrepancies. Furthermore, existing visible-infrared person Re-ID methods struggle to adequately address occlusions and handle background interference effectively. To address the above issues simultaneously, we first construct a new large-scale dataset, Multi-Modality Person Search (MMPS), which tackles the lack of suitable benchmarks for person search in the visible-infrared domain. Encompassing challenges of complex background interferences and occlusions under modality discrepancies, MMPS includes 21,470 images and 1,012 identities across six different cameras. Furthermore, we propose a novel visible-infrared person search method that integrates detection and Re-ID into a progressive process. Specifically, Progressive Inclusion (PI) is proposed to explore backgrounds and provide adaptive proposals. To better tackle the complex occlusions under significant modality discrepancies, we present Discriminative Mix (DM) to synthesize more diverse samples, leveraging specific pattern map embedding. This strategy ensures that our model is not overfitted to specific patterns and is capable of identifying diverse and distinctive human parts. Extensive experiments demonstrate that our method (PI-DM) achieves state-of-the-art performance on the task of visible-infrared person search. Our dataset has been released on https://***/sysuchx/MMPS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Large datasets

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Slew-Driven Layer Assignment for Advanced Non-default-rule Wires 20th

Slew-Driven Layer Assignment for Advanced Non-default-rule W...

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Proceedings of the 20th Web information Systems and Applications Conference, WISA 2023

作者： Lu, Ren Zhang, Wei Jiang, Lieqiu Liu, Genggeng College of Computer and Data Science Fuzhou University Fuzhou350116 China Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350116 China

ISBN: (纸本)9789819962211

With the rapid increase in circuit density in Very Large Scale Integration, the proportion of interconnect delay in circuit timing is also increasing. This makes the importance of layer assignment algorithms increasingly prominent in circuit design. However, most previous layer assignment algorithms prioritize optimizing timing exclusively from the perspective of interconnect delay, thereby disregarding the impact of slew violations on circuits. Therefore, this paper proposes a slew-driven layer assignment algorithm, which considers the timing of different routing layers and introduces non-default-rule wires to design a layer assignment algorithm that can significantly optimize delay, congestion, and slew violations. This algorithm mainly includes three key technologies: 1) Introducing non-default-rule wires technology to optimize timing and adopting a negotiation based approach to ensure that the final routing scheme does not have overflow;2) Proposing a slew prioritization strategy that comprehensively considers slew and interconnect delay during the routing process;3) Proposing a timing critical awareness strategy to further optimize the slew and interconnect delay without worsening the overflow. The experimental results show that the proposed algorithm has significant effects on optimizing delay and reducing slew violations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wire

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Coverage-guided fuzz testing method based on reinforcement learning seed scheduling 4

Coverage-guided fuzz testing method based on reinforcement l...

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2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2024

作者： Tao, Jiaye Hong, Chao Fu, Yun Yang, Yiwei Wei, Lipeng Liang, Zhihong Liu, Junrong Information Centre of Guizhou Power Grid Co Guizhou China Electric Power Research Institute China Southern Power Grid Guangdong Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou China

The existing fuzz testing methods for industrial control protocols suffer from insufficient coverage, false positives, and an inability to handle protocol semantics. This paper proposes a reinforcement learning-based seed scheduling coverage-guided fuzz testing method. Building upon coverage-guided fuzz testing techniques, we integrate reinforcement learning with seed scheduling to optimize the seed selection strategy, thereby enhancing the efficiency of protocol vulnerability detection. Experimental results demonstrate the feasibility and effectiveness of this approach. Through reinforcement learning guidance, seed scheduling is optimized, thereby strengthening the performance of fuzz testing in exploring vulnerabilities in industrial control protocols. © Published under licence by IOP Publishing Ltd.

关键词： Reinforcement learning

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A Continuous Authentication Scheme for Digital Therapeutics Using Denoising Autoencoder 13

A Continuous Authentication Scheme for Digital Therapeutics ...

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13th International Conference on Communication and network security, ICCNS 2023

作者： Wang, Chengling Ma, Yunru Zhang, Yuexin Ye, Ayong Xu, Li College of Computer and Cyber Security Fujian Normal University Provincial Key Laboratory of Network Security and Cryptology Fuzhou China School of Health Fujian Medical University Fujian Provincial Collaborative Innovation Center of Geriatric Rehabilitation and Industry Promotion Fuzhou China

ISBN: (纸本)9798400707964

In healthcare, digital therapeutics provides users with better services in terms of preventing, managing and treating diseases. However, it is a non-trivial task to ensure the security of digital therapeutics. In some scenarios, for example, the wearable biosensors might be used by others or violated by malicious adversaries. When these happen, the patient's treatment might be affected. Investigation shows that conventional one-time login authentication mechanism cannot solve the above problems. Motivated by these observations, in this paper we present a continuous authentication scheme for digital therapeutics using denoising autoencoder. Specifically, it achieves user-transparent re-authentication when users wear the wearable devices. To improve the re-authentication rate, the denoising autoencoder is employed to learn gait features using three-axis acceleration data. To illustrate the practicality, we evaluate the performance of our scheme using public datasets and compare it with that of relevant schemes. The results show that the re-authentication rate of our scheme reaches 92.8%, and it works well no matter users' activities (walking, sitting, running and/or jumping, etc). © 2023 ACM.

关键词： Patient treatment

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Community detection with attributed random walk via seed replacement

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Frontiers of computer Science 2022年第5期16卷 45-56页

作者： Yang CHANG Huifang MA Liang CHANG Zhixin LI College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541004China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Community detection methods based on random walks are widely adopted in various network analysis *** could capture structures and attributed information while alleviating the issues of *** random walks on plain networks have been studied before,in real-world networks,nodes are often not pure vertices,but own different characteristics,described by the rich set of data associated with *** node attributes contain plentiful information that often complements the network,and bring opportunities to the random-walk-based ***,node attributes make the node interactions more complicated and are heterogeneous with respect to topological ***,attributed community detection based on random walk is challenging as it requires joint modelling of graph structures and node *** bridge this gap,we propose a Community detection with Attributed random walk via Seed replacement(CAS).Our model is able to conquer the limitation of directly utilize the original network topology and ignore the attribute *** particular,the algorithm consists of four stages to better identify communities.(1)Select initial seed nodes in the network;(2)Capture the better-quality seed replacement path set;(3)Generate the structure-attribute interaction transition matrix and perform the colored random walk;(4)Utilize the parallel conductance to expand the *** on synthetic and real-world networks demonstrate the effectiveness of CAS.

关键词： community detection seeds colored random walk parallel conductance

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SCREADER: Prompting Large Language Models to Interpret scRNA-seq Data 24

SCREADER: Prompting Large Language Models to Interpret scRNA...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Li, Cong Long, Qingqing Zhou, Yuanchun Xiao, Meng Institute of Zoology Chinese Academy of Sciences State Key Laboratory of Stem Cell and Reproductive Biology China University of Chinese Academy of Sciences China Chinese Academy of Sciences Computer Network Information Center China

ISBN: (纸本)9798331530631

Large language models (LLMs) have demonstrated remarkable advancements, primarily due to their capabilities in modeling the hidden relationships within text sequences. This innovation presents a unique opportunity in the field of life sciences, where vast collections of single-cell omics data from multiple species provide a foundation for training foundational models. However, the challenge lies in the disparity of data scales across different species, hindering the development of a comprehensive model for interpreting genetic data across diverse organisms. In this study, we propose an innovative hybrid approach that integrates the general knowledge capabilities of LLMs with domain-specific representation models for single-cell omics data interpretation. We begin by focusing on genes as the fundamental unit of representation. Gene representations are initialized using functional descriptions, leveraging the strengths of mature language models such as LLaMA-2. By inputting single-cell gene-level expression data with prompts, we effectively model cellular representations based on the differential expression levels of genes across various species and cell types. In the experiments, we constructed developmental cells from humans and mice, specifically targeting cells that are challenging to annotate. We evaluated our methodology through basic tasks such as cell annotation and visualization analysis. The results demonstrate the efficacy of our approach compared to other methods using LLMs, highlighting significant improvements in accuracy and interoperability. Our hybrid approach enhances the representation of single-cell data and offers a robust framework for future research in cross-species genetic analysis. 1 © 2024 IEEE.

关键词： Data Mining Large Language Model Prompt Learning Single Cell RNA Data

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