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Outsourced Secure Cross-Modal Retrieval based on Secret Sharing for Lightweight Clients

IEEE Internet of Things Journal 2025年

作者： Niu, Ziyu Wang, Hao Li, Zhi Su, Ye Xu, Lijuan Zhang, Yudi Susilo, Willy Shandong Normal University School of Information Science and Engineering Jinan China University of Wollongong School of Computing and Information Technology Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education National Supercomputer Center in Jinan Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

Cross-modal retrieval is a technique that uses one modality to query another modality in multimedia data (e.g., retrieving images based on text, or retrieving text based on images). It can break down the barriers between different modalities and achieve seamless information connection. Secure cross-modal retrieval focuses on privacy issues in cross-modal retrieval, including private data of data owners and private query requests of users. Current work on secure cross-modal retrieval protects private information through homomorphic encryption, which makes the efficiency of the retrieval phase not ideal. Therefore, the conflict between retrieval efficiency and security has become an important issue that needs to be resolved in secure cross-modal retrieval. We propose a scheme to achieve secure cross-modal retrieval in the form of secret sharing in the IoT environment. In the scheme, the data owner (DO) can secretly divide all the original data into two parts and upload them to two non-collusive cloud servers respectively. The servers store the data and provide cross-modal retrieval for users. The security of the scheme is proved under semi-honest model, and the experiments show that our scheme is more efficient than previous work in the search phase. When the query dimension is 512 and the number of latent factors is 500, the search time is reduced by more than half compared with previous work. © 2014 IEEE.

关键词： Sensitive data

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A trajectory association strategy for multi-object tracking 6

A trajectory association strategy for multi-object tracking

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6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Ma, Sugang Duan, Shuaipeng Pu, Lei Hou, Zhiqiang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China School of Operational Support Rocket Force Engineering University Shaanxi Xi'an710025 China

ISBN: (纸本)9798400707674

When the multi-object tracking (MOT) algorithm confronts complex scenarios such as target occlusion and blurring, the trajectory missing and identity switching problems frequently occur. To address this issue, a trajectory association strategy for MOT is presented. First, using the similarity relationship between two frames, the similarity matrix is introduced to predict a more trustworthy offset vector and lower the number of Identity Switch. Then, a trajectory reconnection strategy is provided in order to retain a portion of the low-score detection frames due to occlusion and to perform secondary matching on the low-score detection frames in order to reduce missing trajectories. On a typical multi-object tracking dataset MOT17, experimental comparison study is performed with several known advanced algorithms, and MOTA and IDF1 achieve 67.9% and 67.8%, respectively. The experimental results show that the proposed algorithm better solves the trajectory missing and identity switching problems, and achieves excellent tracking performance in complex scenarios. © 2023 ACM.

关键词： Trajectories

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Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social networks

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Big Data Mining and Analytics 2021年第4期4卷 242-251页

作者： Xueting Liao Danyang Zheng Xiaojun Cao Department of Computer Science Georgia State UniversityAtlantaGA 30302USA Suzhou Key Laboratory of Advanced Optical Communication Network Technology School of Electronic and Information EngineeringSoochow UniversitySuzhou 215006China

The COVID-19 pandemic has hit the world *** reaction to the pandemic related issues has been pouring into social platforms,such as *** public officials and governments use Twitter to make policy *** keep close track of the related information and express their concerns about the policies on *** is beneficial yet challenging to derive important information or knowledge out of such Twitter *** this paper,we propose a Tripartite Graph Clustering for Pandemic Data Analysis(TGC-PDA)framework that builds on the proposed models and analysis:(1)tripartite graph representation,(2)non-negative matrix factorization with regularization,and(3)sentiment *** collect the tweets containing a set of keywords related to coronavirus pandemic as the ground truth *** framework can detect the communities of Twitter users and analyze the topics that are discussed in the *** extensive experiments show that our TGC-PDA framework can effectively and efficiently identify the topics and correlations within the Twitter data for monitoring and understanding public opinions,which would provide policy makers useful information and statistics for decision making.

关键词： COVID-19 clustering online social network Twitter

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A Multi-Channel Composite Recommendation Method in Federated Learning

A Multi-Channel Composite Recommendation Method in Federated...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Chen, Shi Li, Qianmu Wang, Qi Zhao, Xinjian Ge, Hao Li, Nianzhe State Grid Jiangsu Information & Telecommunication Company Nanjing China School of Computer Science Ana Engineering Nanjing University of Science and Technology Nanjing China State Grid Smart Grid Research Institute Co. Ltd. State Grid Key Laboratory of Information & Network Security Nanjing China

ISBN: (纸本)9798350304602

There are various data security risks in power grid systems, and recommendation systems are a good solution to the selection of security strategies. Federated learning is a machine learning framework that can protect the data privacy of each participant and can effectively solve the data security external collaborative training modeling challenges. This paper proposed a flexible implantable recommendation algorithm based on knowledge graph and multi-channel composite to enhance federated learning on demand. The algorithm designs three different feature extraction modules to learn to construct multi-dimensional features on multi-attribute collaborative knowledge graphs. Among them, the distance translation module utilizes the Trans R recommendation algorithm to learn the internal semantic structure information in knowledge graph triples and terminal-security policy selection history;the random walk module uses the Node2Vec algorithm to learn the homogeneous and Structured information in the multi-attribute collaborative knowledge graph;the graph convolutional network module learns the high-order neighborhood multi-attribute collaborative information of terminal/security policy nodes through multi-layer feature propagation and aggregation. The composite network module adopts a convolutional neural network-like operation to first locally optimize the three modules, and then extracts the highly informative elements to obtain the final feature representation of the terminal/security policy. Finally, the algorithm merges the vectors of terminal-security policy pairs into the multi-layer perceptron to learn and calculate the prediction score for recommendation. © 2023 IEEE.

关键词： Recommender systems

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Local Fault Diagnosis Analysis Based on Block Pattern of Regular Diagnosable networks

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IEEE/ACM Transactions on networking 2024年

作者： Lin, Limei Guan, Kaineng Huang, Yanze Hsieh, Sun-Yuan Chen, Gaolin Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

Fault diagnosability can reflect the actual self diagnosing capability of a multiprocessor system better. However, people usually focus on the overall information and neglect the important local information. In order to reflect the locality of a system at a node better, this paper proposes a novel fault diagnosis strategy, called x-block local fault diagnosability (x-BLFD), where the x-block condition requires more than x connected fault-free nodes. Then, we characterize some important properties about the x-BLFD of multiprocessors interconnected networks under the Preparata/Metze/Chien model (P/M/C), and further propose the x-BLFD in an f(x)-extended block network with the minimum (x+1)-subnetwork degree at some node. We also establish an approximate algorithm to calculate the x-BLFD of a large-scale diagnosable network at some node, and analyze the experimental performance of large-scale networks. Furthermore, we apply our proposed conclusion to obtain the x-BLFD of 16 well-known networks at some node directly under P/M/C, including dual cubes, hierarchical cubic networks, DQcubes, twisted hypercubes, Bicube networks, crossed cubes, folded hypercubes, k-ary n-cubes, balanced hypercubes, BC graphs, (n,k)-star graphs, Cayley graphs generated by transposition trees, bubble-sort star graphs, split-star networks, data center networks, and (n,k)-arrangement graphs. Finally, we compare the x-BLFD with the diagnosability, conditional diagnosability, pessimistic diagnosability, and t/k-diagnosability by a large number of detailed numerical analysis. It can be seen that the x-BLFD is greater than all the other types of fault diagnosabilities. © 1993-2012 IEEE.

关键词： Hypercube networks

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An Efficient Multiband Infrared Small Objects Detection Approach for Low-Altitude Artificial Intelligence of Things

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IEEE Internet of Things Journal 2025年第12期12卷 20757-20772页

作者： Peng, Yuhuai Wang, Jing Wang, Wenqian Liu, Lei Atiquzzaman, Mohammed Guizani, Mohsen Dustdar, Schahram Northeastern University School of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Strategic Research Department Hangzhou310024 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Xi’an University of Posts and Telecommunications Shanxi Key Laboratory of Information Communication Network and Security Shanxi Xi’an710121 China University of Oklahoma School of Computer Science NormanOK73019 United States Mohamed bin Zayed University of Artificial Intelligence Department of Machine Learning Abu Dhabi United Arab Emirates Technische Universität Wien Distributed Systems Group Vienna1040 Austria Universitat Pompeu Fabra Barcelona ICREA Barcelona08002 Spain

As a cutting-edge technology of low-altitude Artificial Intelligence of Things (AIoT), autonomous aerial vehicle object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multiband infrared small object detection approach for low-altitude intelligent surveillance services. First, we propose the multiband infrared image fusion algorithm based on cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based multiscale dense small object detection (MsDSOD) algorithm is proposed. The algorithm consists of the global-local object detection (G-LOD) network, the object dense area extraction (O-DAE) module, and the weighted boxes fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean average precision (mAP) of MsDSOD is improved by 0.80% and the average precision in small object detection (APs) is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion. © 2014 IEEE.

关键词： Infrared imaging

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Task Offloading and Resource Allocation with Privacy Constraints in End-Edge-Cloud Environment 17th

Task Offloading and Resource Allocation with Privacy Constra...

引用

17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Zhu, Xia Sun, Wei Li, Xiaoping School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9789819923847

The end-edge-cloud architecture brings an efficient solution to the big data processing problem caused by massive IoT devices. The characteristics of this architecture, such as massive devices, heterogeneous resources, and complex layers, bring new challenges to privacy protection issues. This paper considers the cooperation among terminal devices, edge nodes, and remote clouds to solve the task scheduling problem with privacy constraints by optimizing the offloading decision and resource allocation. A heuristic privacy-aware task offloading and resource allocation algorithm is proposed to maximize the number of successful tasks, which offloads low-privacy and non-privacy tasks to find sub-optimal offloading decisions by offloading sequence generation rules and decision adjustment. Task scheduling algorithms are presented by communication and computing resource allocation strategies for low-privacy and non-privacy tasks. ANOVA technique is used to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm is superior to others in terms of the number of devices, the amount of different task data, and the proportion of privacy tasks at different levels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing architecture

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On the Impact of Spatial Mode Dispersion for Strongly-Coupled Multicore Fiber Submarine Transmission

On the Impact of Spatial Mode Dispersion for Strongly-Couple...

引用

2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Sun, Lin Chen, Bin Liu, Gordon Ning Cai, Yi Li, Zhaohui Lu, Chao Shen, Gangxiang Soochow University Suzhou Key Laboratory of Advanced Optical Communication Network Technology School of Electronic and Information Engineering Suzhou215006 China Hefei University of Technology School of Computer Science and Information Engineering Hefei China Sun Yat-Sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips School of Electronics and Information Technology Guangzhou511400 China The Hong Kong Polytechnic University Photonics Research Center Department of Electrical and Information Engineering Kowloon Hung Hom Hong Kong

Strongly-coupled multicore fibers exhibit the improved tolerance to fiber non-linearity. Their potentials in optical submarine communications are investigated with considering the coupling length and spatial mode disp... 详细信息

ISBN: (纸本)9781957171180

关键词： Submarines

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Incentive Sharing: A Consumer-Data Privacy Trusted Sharing Paradigm Via Proximal Policy Optimization in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Cai, Ting Hu, Ying Li, Xiang Chen, Wuhui Wu, Yuxin Ye, Zhiwei Zheng, Zibin Hubei University of Technology China China Hubei University of Technology School of Computer Science The Provincial Key Laboratory of Green Intelligent Computing Network Wuhan430068 China Sun Yat-sen University China Pengcheng Laboratory China Hubei University of Arts and Science School of Computer Engineering Xiangyang441053 China Sun Yat-sen University School of Software Engineering Zhuhai519000 China

Industry 5.0 emphasizes consumer data sharing to drive data-driven innovation and enhance user experiences in consumer electronics. Blockchain-integrated federated learning (blockchained FL) has recently emerged as a privacy-Trusted paradigm to facilitate sharing. However, most blockchained FL sharing paradigms cannot adapt to the complexity or variability of consumer data and network conditions such as: 1) inadequate balancing of the trade-off between data quality and training costs during device selection;2) incentives are designed to maximize instantaneous profit without considering the long-Term dynamics of electronic devices. These limitations lead to low-quality model training and security degradation. This paper proposes a novel blockchained FL incentive-sharing paradigm that dynamically selects consumer devices from a long-Term perspective while balancing data quality and training costs. Specifically, we first present a reputation strategy to enhance security by applying a predefined threshold to prevent malicious and low-quality consumers from participating in FL. Then, to optimize device selection under complex and dynamic consumer environments with high-dimensional system states, a Proximal Policy Optimization (PPO)-based incentive mechanism is proposed, which includes: 1) building an 'intelligent engine' to select the optimal consumer devices for each round of FL based on data quality and costs, and 2) implementing incentives to achieve long-Term maximization of the system's social welfare. Extensive simulations show that our sharing paradigm has, on average, 46.97% higher social welfare and 77.27% lower social costs than baselines and can guarantee high data quality in a trustless environment. © 1975-2011 IEEE.

关键词： Differential privacy

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SyntaxBridge: Protocol Description Transformer for Enhanced Formal Analysis of Security Protocols

SyntaxBridge: Protocol Description Transformer for Enhanced ...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Liujia Cai Tong Yu Yumeng Li Siqi Lu Hanjie Dong Guangying Cai Guangong Li Yongjuan Wang Henan Key Laboratory of Network Cryptography Technology Information Engineering University Zhengzhou China School of Information and Communication Engineering Hainan University Haikou China School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Currently, formal analysis of security protocols under the symbolic model has become a crucial approach in the field of protocol analysis, owing to its intuitiveness and universality. However, researchers using various symbolic security protocol verifiers need to thoroughly understand the strengths and weaknesses of these verifiers, as well as their specific encoding models. A&B protocol description and CAS+ protocol description are highly favored by researchers due to their intuitiveness, but their limitation lies in the fact that these two descriptions can only be used for their corresponding verifiers. In this study, we constructed a bridge connecting different symbolic security protocol verifiers. Building upon existing compilers A&B to TAMARIN and CAS+ to AVISPA, we extended A&B to CAS+ and CAS+ to A&B using the lexical analyzer Flex and the parser generator tool Bison. Thus, this bridge enables leveraging the respective strengths of each symbolic security protocol verifier in a short period. It also greatly facilitates security protocol researchers in analyzing protocols using intuitive protocol descriptions through various formal analysis tools.

关键词： Bridges Analytical models Privacy Protocols Flexible printed circuits Transformers Generators Encoding Grammar Security

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