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Location Privacy Preservation Crowdsensing With Federated Reinforcement Learning

IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 1877-1894页

作者： You, Zhichao Dong, Xuewen Liu, Ximeng Gao, Sheng Wang, Yongzhi Shen, Yulong Xidian University School of Computer Science and Technology The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi'an710071 China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Fuzhou University College of Computer and Data Science Fuzhou350025 China Central University of Finance and Economics School of Information Beijing102206 China California State Polytechnic University Pomona Department of Computer Information Systems PomonaCA91768 United States Xidian University School of Computer Science and Technology Xi'an710071 China Shaanxi Key Laboratory of Network and System Security Xi'an710071 China

Crowdsensing has become a popular method of sensing data collection while facing the problem of protecting participants' location privacy. Existing location-privacy crowdsensing mechanisms focus on static tasks and participants without considering sensing tasks' time requirements and participants' mobility, which cannot achieve satisfactory collected data quality and task completion in crowdsensing with dynamic tasks and participants. Inspired by this, we proposed a location-preservation crowdsensing mechanism, FedSense, considering dynamic tasks and participants based on federated learning (FL) and reinforcement learning (RL). In FedSense, through RL's outstanding decision-making ability, participants select sensing tasks to perform by well-trained RL models without uploading location information to servers for task allocation. We propose an independent tasks selection environment that defines actions, states, and rewards of RL to enable FedSense to achieve satisfactory task completion and data quality while preserving location privacy. Besides, FedSense applies an asynchronous FL aggregation algorithm that reduces participants' network stabilization and device computing ability requirements. Analysis proves that participants' location information does not leave the local device during the model training and task selection process, effectively avoiding privacy leakage. Simulation shows that compared with existing location-preservation crowdsensing mechanisms, FedSense achieves the highest task completion and sensing accuracy for dynamic tasks and participants. © 2024 IEEE.

关键词： Data privacy

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DiRLoc: Disentanglement Representation Learning for Robust Image Forgery Localization

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2841-2854页

作者： Sheng, Ziqi Qu, Zuomin Lu, Wei Cao, Xiaochun Huang, Jiwu Sun Yat-sen University School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Shenzhen518107 China Shenzhen MSU-BIT University Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen518116 China

Deep Learning image forgery localization methods have achieved remarkable results but cannot maintain comparable performance when the forgery images are JPEG compressed, a format that is widely used in daily information transmission. The robustness against JPEG compression has become a bottleneck to the practical application of image forgery localization. To address this issue, a robust image forgery localization framework is proposed against the performance degradation caused by JPEG compression. Specifically, a cutting-edge progressive disentanglement strategy is proposed that incorporates coarse-grained image disentanglement to mitigate the detrimental effects of general JPEG compression, while harnessing the ability of fine-grained element disentanglement to separate multi-scale artifacts, thereby minimizing interference from content information. Moreover, the decision strategy is carefully designed to reinforce subtle signals from tampered areas, including artifacts fusion block reasoning multi-scale artifacts and dual attention block that learn more about forgery-related features. Extensive visualizations and experiments demonstrate that our method can achieve competitive performance in general JPEG-resistant image forgery localization, especially in the performance of generalization experiments. © 2004-2012 IEEE.

关键词： Image compression

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An Improved Multi-objective Particle Swarm Optimization Algorithm with Reduced Initial Search Space 21st

An Improved Multi-objective Particle Swarm Optimization Algo...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Zhiguang, Chu Yingchen, Fan Xiaolei, Zhang Ruyan, Zhang Xing, Zhang School of Electronics and Information Engineering Liaoning University of Technology Jinzhou121000 China Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Jinzhou121000 China

ISBN: (纸本)9789819601240

As data dimensions continue to increase, the challenges of data storage become more severe. While feature selection can reduce the complexity of high-dimensional data, existing methods are prone to local optima and high computational costs. To address these issues, this paper proposes a hybrid feature selection method based on MOPSO (Multi-Objective Particle Swarm Optimization). This method first uses a filter approach to narrow the search space, then employs a particle swarm algorithm to generate particles, incorporating mutation, crossover, and selection strategies during particle updates to enhance their search capabilities. Additionally, an archive set is established, utilizing grid-based and density-based methods during the update process to avoid local optima. Finally, the proposed method is compared with two single-objective optimization methods and two multi-objective optimization methods on multiple datasets. Experimental results demonstrate that the proposed method significantly reduces classification error rates and effectively reduces the number of features, providing an efficient solution for feature selection in high-dimensional data and addressing the challenges in high-dimensional data analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Contract-Based Incentive Mechanisms for UAV networks in Covert Communications

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7378-7391页

作者： Tian, Wen Fan, Hao Liu, Guangjie Du, Miao Liu, Yinqiu Niyato, Dusit Han, Zhu Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing University of Information Science and Technology Key Laboratory of Intelligent Support Technology In Complex Environment Ministry of Education Nanjing210044 China Southeat University School of Computer Science and Engineering China Southeast University Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Covert communication is considered a promising technology for hiding transmission processes and activities from malicious eavesdropping. With the development of detection technology, the traditional point-to-point covert communication method cannot effectively guarantee the reliability of covert communication. In order to solve this problem, unmanned aerial vehicle (UAV) are widely adopted in covert communication as common relay equipment. However, the UAV maximizes its benefits by taking advantage of the information asymmetry between itself and the covert communication sender. Therefore, it is necessary to encourage UAV-assisted potential users (UPU) to transmit covert data honestly by designing an information asymmetry-based incentive mechanism. In this paper, we propose a UAV-assisted contract-theoretic (UACT) model to improve the covert communication performance, where UPU honestly transmits covert data and the throughput of UACT can be increased. Specifically, we first divide UPU into finite types and model the data transfer contract between the sender, UPU, and receiver. Then, the contract feasibility of UACT is derived with sufficient and necessary conditions. Finally, we analyze the contract optimization in the continuous case of UPUs. Numerical simulations show that the UACT can incentivize UPUs to transmit covert data through UAV assistance and make covert communication performance close to the information symmetry case. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Don’t Abandon the Primary key: A High-Synchronization and Robust Virtual Primary key Scheme for Watermarking Relational Databases 26th

Don’t Abandon the Primary Key: A High-Synchronization and...

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26th International Conference on information and Communications security, ICICS 2024

作者： Yang, Ke Yuan, Shuguang Yu, Jing Wang, Yuyang Yang, Tengfei Chen, Chi Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Cyberspace Security Defense Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

ISBN: (纸本)9789819788002

A relational database is an infrastructure that manages and shares structured data. To safeguard the copyrights of data within such databases, database watermarking is an effective technique. Most watermarking schemes rely on the primary key (PK) to locate and embed watermarks, which preserves high watermark synchronization during detection. However, these schemes become invalid once PK is erased or changed. To avoid this vulnerability, virtual primary key (VPK) schemes are proposed to replace PK. Nevertheless, duplicate values in virtual primary keys compromise synchronization. Besides, current VPK schemes fail to utilize the primary key, even when it remains unchanged. This strategy decreases detection accuracy in many cases. In addition, we find an attribute name attack. It is a common challenge of existing watermarking schemes, which distort the link between detected attributes and watermarked attributes. In this paper, we propose a high-synchronization and robust VPK scheme. It introduces a classifier to maintain the original order and number of attributes to resist attribute name attacks. To resist primary key erasure or change and mitigate synchronization problems, it generates distinct virtual primary keys. Moreover, to improve detection accuracy, our watermarking scheme integrates the primary key when it remains unchanged. Experiments demonstrate that our scheme achieves high watermark synchronization. It is robust against various attacks, even when 98% of tuples or over half of the attributes are distorted. It is also practical in terms of data distortion and overhead. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Relational databases Virtual primary key Watermarking

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New Construction of Code-Based Signature Schemes 26th

New Construction of Code-Based Signature Schemes

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26th International Conference on information and Communications security, ICICS 2024

作者： Yang, Yang Zhang, Fangguo School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9789819788002

In this paper, we present a novel approach to construct code-based digital signature schemes. Our focus is on addressing the efficiency issues stemming from the low proportion of decodable syndromes in classical CFS signature scheme. To overcome this challenge, we introduce a well-defined syndrome subspace wherein all syndromes can be efficiently decoded. Additionally, our scheme differs from traditional code-based approaches by employing a novel method for constructing public and private keys, replacing permutation matrices with invertible matrices. This transformation yields a public key matrix that is statistically indistinguishable from a random matrix. We demonstrate that our scheme achieves existential unforgeability under an adaptive chosen message attack (EUF-CMA) in the random oracle model. Comparative analysis against other code-based signature schemes, such as Wave and Enhanced pqsigRM, reveals significant advantages in both public key and signature sizes. Furthermore, compared to the three post-quantum signature schemes (Crystals-Dilithium, Falcon, and Sphincs) selected as finalists by NIST PQC, our scheme still maintains a significant advantage in terms of signature size. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quantum cryptography

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Electronic band structures of topological kagome materials

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Chinese Physics B 2025年第1期34卷 29-44页

作者： Man Li Huan Ma Rui Lou Shancai Wang School of Information Network Security People’s Public Security University of ChinaBeijing 100038China Department of Physics Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)and Beijing Key Laboratory of Opto-electronic Functional Materials&Micronano DevicesRenmin University of ChinaBeijing 100872China Leibniz Institute for Solid State and Materials Research IFW Dresden01069 DresdenGermany Helmholtz-Zentrum Berlin fur Materialien und Energie Albert-Einstein-Strabe 1512489 BerlinGermany Joint Laboratory“Functional Quantum Materials”at BESSY II 12489 BerlinGermany

The kagome lattice has garnered significant attention due to its ability to host quantum spin Fermi liquid ***,the combination of unique lattice geometry,electron–electron correlations,and adjustable magnetism in solid kagome materials has led to the discovery of numerous fascinating quantum *** include unconventional superconductivity,charge and spin density waves(CDW/SDW),pair density waves(PDW),and Chern insulator *** emergent states are closely associated with the distinctive characteristics of the kagome lattice's electronic structure,such as van Hove singularities,Dirac fermions,and flat bands,which can exhibit exotic quasi-particle excitations under different symmetries and magnetic ***,various quantum kagome materials have been developed,typically consisting of kagome layers stacked along the z-axis with atoms either filling the geometric centers of the kagome lattice or embedded between the *** this topical review,we begin by introducing the fundamental properties of several kagome *** gain an in-depth understanding of the relationship between topology and correlation,we then discuss the complex phenomena observed in these *** include the simplest kagome metal T_(3)X,kagome intercalation metal T X,and the ternary compounds AT_(6)X_(6)and RT_(3)X_(5)(A=Li,Mg,Ca,or rare earth;T=V,Cr,Mn,Fe,Co,Ni;X=Sn,Ge;R=K,Rb,Cs).Finally,we provide a perspective on future experimental work in this field.

关键词： kagome lattice quasi-particle excitation electronic correlation magnetism

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Hedonic Coalition Formation Game and Contract-Based Federated Learning in AAV-Assisted Internet of Things

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IEEE Internet of Things Journal 2025年第9期12卷 11258-11272页

作者： Liu, Jia Li, Xiaoxu Xu, Yang Lyu, Chen Wang, Yichuan Liu, Xiaoying Xidian University School of Computer Science and Technology Xi’an710071 China Shanghai University of Finance and Economics School of Computing and Artificial Intelligence MoE Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai200433 China Xi’an University of Technology Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an710071 China Zhejiang University of Technology School of Computer Science and Technology Hangzhou310023 China

Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Things (IoT) components enable effective artificial intelligence (AI)-based models to be built. Beyond ground data sources, autonomous aerial vehicles (AAVs)-based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), have become increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impede data sharing across independently owned AAVs. To this end, we propose in this article a federated learning (FL)-based architecture that enables privacy-preserving collaborative machine learning across a federation of independent DaaS providers for the development of IoT applications. Specifically, this work introduces a novel incentive mechanism based on the hedonic coalition formation game to enhance the sustainable efficiency and stability of the FL system. By establishing tailored operational rules and functions, the proposed mechanism enables IoT sensing nodes to autonomously form optimal coalitions with AAVs, thereby ensuring robust collaboration. To deal with incentive mismatches and information asymmetry, we leverage the contract theory and propose a self-disclosure mechanism that guarantees truthful reporting of AAV capabilities while optimizing the global model owner’s profits. The performance-based AAV type is also defined to offer a practical measure for heterogeneous AAVs and serve as the foundation for fair and effective contract design. Simulation results validate the superiority of the proposed approach, demonstrating significant improvements in utility optimization and system stability compared to existing benchmarks. © 2014 IEEE.

关键词： Federated learning

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Anomaly Detection in network Access-Using LSTM and Encoder-Enhanced Generative Adversarial networks

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第7期49卷 175-186页

作者： Hu, Jian He, Yingjun Xu, Wenqian Jiang, Yixin Liang, Zhihong Yang, Yiwei Yunnan Power Grid Information Center China Southern Power Grid Kunming650217 China Electric Power Research Institute China Southern Power Grid Guangzhou510663 China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou510663 China

Along with the continuous development of information technology, the database has become an important module for enterprises and individuals to apply computers, and some important data are stored in the database, which also leads to the database becoming the target of malicious intruders. The abnormal access behavior detection algorithm for data can quickly identify abnormal access situations, timely intervention and processing to ensure data security. Based on this, this paper proposes an abnormal defense behavior detection algorithm based on generative adversarial network, the new algorithm has the applicability as well as two derivative models of generative adversarial network for network abnormal access detection with high efficiency. In this paper, we experiment the classification accuracy of network anomaly detection algorithm, i.e., F1, by using three models, namely, the original Generative Adversarial network (GAN), Generative Adversarial network using Long and Short-Term Memory network (GAN+LSTM), and Generative Adversarial network with the addition of an encoder (GAN+Encoder), and the result of this paper shows that the GAN+Encoder model is the most effective. And based on these three models of the generator and discriminator loss trends are compared, as well as through the iteration of 50 times of training results show that the GAN + Encoder model is relatively simple, and the training time is shorter and more efficient. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Generative adversarial networks

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BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme with Fast Encryption and Outsourced Decryption for IoT 24th

BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Huang, Yao Yu, Zhongyuan Wang, Guijuan Chai, Baobao Zhang, Hongliang Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819615414

Many sophisticated cloud platforms are available to provide services such as storage and computation for Internet of Things (IoT) applications. However, with the concerns about the semi-trusted nature of cloud platforms, data security and privacy have been gaining broad attention. In this paper, we propose a blockchain-assisted verifiable data sharing scheme (BAV-DSS) for IoT. The BAV-DSS features fine-grained access control, fast encryption, and outsourced decryption with verifiability. Specifically, with the application of ciphertext-policy attribute-based encryption (CP-ABE), flexible access control is enabled. Blockchain is employed to store public keys, generate account addresses, and perform data verification. Furthermore, the selective replayable chosen ciphertext attacks (sRCCA) security of BAV-DSS is proved, and the simulation results indicate that BAV-DSS obtains high efficiency and practicality compared to other schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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