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Privacy Preserving Algorithm for Spectrum Sensing in Cognitive Vehicle Networks

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Chinese Journal of Electronics 2024年第1期33卷 30-42页

作者： Hongning LI Tonghui HU Jiexiong CHEN Xiuqiang WU Qingqi PEI guangzhou institute of Technology Xidian University guangzhou CEPREI Shaanxi Key Laboratory of Blockchain and Secure Computing

The scarcity of spectrum resources fails to meet the increasing throughput demands of vehicular networks. There is an urgent need to maximize the utilization of spectrum bands in mobile networks. To ascertain the availability of spectrum bands, users should engage in wireless channel sensing and collaboration. However, spectrum sensing data always involves users’ privacy, such as their location. This paper first introduces sensing trajectory inference attack in cognitive vehicular networks and then proposes a data confusion-based privacy-preserving algorithm and a cryptonym array-based privacy-preserving aggregation scheme for spectrum sensing in cognitive vehicular networks. Unlike existing methods, the proposed schemes transmit confused data during the aggregation process. This deliberate obfuscation makes it almost impossible to infer users’ location from the transmitted data. The analysis demonstrates the resilience of the proposed schemes against sensing trajectory inference attack.

关键词： Privacy Data privacy Wireless sensor networks Authentication Data aggregation Throughput Inference algorithms

Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2297-2314页

作者： Tianyu Wang Kongyang Chen Department of Cyberspace Security Guangzhou UniversityGuangzhou51006China institute of Artificial Intelligence Guangzhou UniversityGuangzhou51006China Center of Young Scholars Pazhou LabGuangzhou510335China

Crowdsourcing holds broad applications in information acquisition and dissemination,yet encounters challenges pertaining to data quality assessment and user reputation *** mechanisms stand as crucial solutions for appraising and updating participant reputation scores,thereby elevating the quality and dependability of crowdsourced ***,these mechanisms face several challenges in traditional crowdsourcing systems:1)platform security lacks robust guarantees and may be susceptible to attacks;2)there exists a potential for large-scale privacy breaches;and 3)incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations,occasionally lacking a dedicated reputation update *** paper introduces a reputation update scheme tailored for crowdsourcing,with a focus on proficiently overseeing participant reputations and alleviating the impact of malicious activities on the sensing ***,the reputation update scheme is determined by an Empirical Cumulative distribution-based Outlier Detection method(ECOD).Our scheme embraces a blockchain-based crowdsourcing framework utilizing a homomorphic encryption method to ensure data transparency and *** of user reputation scores relies on their behavioral history,actively discouraging undesirable ***,we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation,enabling the system to allocate incentives based on user behavior and *** scheme undergoes evaluation on 11 datasets,revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious *** research not only presents a practical solution for crowdsourcing reputation management but also offers valuable insights for future research and applications,holding promise for fostering more reliable and high-quality data collection in

关键词： Crowdsourcing reputation management blockchain

Model architecture level privacy leakage in neural networks

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Science China(Information Sciences) 2024年第3期67卷 20-32页

作者： Yan LI Hongyang YAN Teng HUANG Zijie PAN Jiewei LAI Xiaoxue ZHANG Kongyang CHEN Jin LI institute of Artificial Intelligent and Blockchain Guangzhou University Pazhou Lab

Privacy leakage is one of the most critical issues in machine learning and has attracted growing interest for tasks such as demonstrating potential threats in model attacks and creating model *** recent years, numerous studies have revealed various privacy leakage risks(e.g., data reconstruction attack, membership inference attack, backdoor attack, and adversarial attack) and several targeted defense approaches(e.g., data denoising, differential privacy, and data encryption). However, existing solutions generally focus on model parameter levels to disclose(or repair) privacy threats during the model training and/or model interference process, which are rarely applied at the model architecture level. Thus, in this paper, we aim to exploit the potential privacy leakage at the model architecture level through a pioneer study on neural architecture search(NAS) paradigms which serves as a powerful tool to automate a neural network *** investigating the NAS procedure, we discover two attack threats in the model architecture level called the architectural dataset reconstruction attack and the architectural membership inference attack. Our theoretical analysis and experimental evaluation reveal that an attacker may leverage the output architecture of an ongoing NAS paradigm to reconstruct its original training set, or accurately infer the memberships of its training set simply from the model architecture. In this work, we also propose several defense approaches related to these model architecture attacks. We hope our work can highlight the need for greater attention to privacy protection in model architecture levels(e.g., NAS paradigms).

关键词： neural architecture search data reconstruction attack membership inference attack

Cyber-Physical Resilience Enhancement for Power Transmission Systems with Energy Storage Systems

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 844-855页

作者： Wenhao Zhang Dongyang Rui Weihong Wang Yang Guo Zhaoxia Jing Wenhu Tang School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China guangzhou Zhiguang Electric Co. Ltd and Zhiguang Research Institute(Guangzhou)Co.LtdGuangzhou 510765China

In a power system, when extreme events occur, such as ice storm, large scale blackouts may be unavoidable. Such small probability but high risk events have huge impact on power systems. Most resilience research in power systems only considers faults on the physical side, which would lead to overly idealistic results. This paper proposes a two-stage cyber-physical resilience enhancement method considering energy storage (ES) systems. The first stage calculates optimal planning of ES systems, and the second stage assesses resilience and enhancement of ES systems during the disaster. In the proposed model, cyber faults indirectly damage the system by disabling monitoring and control function of control center. As a result, when detection and response process of physical faults are blocked by cyber failures, serious load shedding occurs. Such a cyber-physical coupling mechanism of fault, response, restoration process is demonstrated in the modified IEEE Reliable Test System-79 (RTS-79). Simulation results show compared with the physical-only system, the cyber-physical system has a more accurate but degraded resilient performance. Besides, ES systems setting at proper place effectively enhance resilience of the cyber-physical transmission system with less load Shedding.

关键词： Cyber-physical faults energy storage systems resilience enhancement transmission systems

The function of doping nitrogen on removing fluoride with decomposing La-MOF-NH_2:Density functional theory calculation and experiments

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Journal of Environmental Sciences 2024年第1期135卷 118-129页

作者： Hongguo Zhang KuilinWan Jia Yan Qian Li Yufang Guo Lei Huang Samuel Raj Babu Arulmani Jian Luo School of Environmental Science and Engineering Guangzhou UniversityGuangzhou 510006China guangzhou University-Linkoping University Research Center on Urban Sustainable Development Guangzhou UniversityGuangzhou 510006China School of Civil and Environmental Engineering Georgia Institute of TechnologyAtlantaGA 30332USA

Fluoride is an important pollutant in wastewater,and adsorption is an effective way to remove *** nitrogen plays an important role in adsorbent materials,computational models were developed to understand the changes in work function resulting from nitrogen ***-N-C-800℃,was prepared by pyrolyzing La-MOF-NH_(2)to verify the influence on the performance of removing fluoride by *** and electrochemical performance tests were performed to characterize La-N-C-800℃.Adsorption kinetics,adsorption thermodynamics,initial concentrations,pH,and ions competition were investigated using La-N-C-800℃for fluoride *** addition,density functional theory was applied to evaluate the function of *** nitrogen atoms were added,the density of states,partial density of states,populations,and different orbits of charge were calculated to discover deep *** strengthened the carbon structure and La_(2)O_(3)structure to remove *** addition,nitrogen can also act as an adsorption site in the carbon *** results provide design ideas for improving the performance of adsorbent materials by doping elements.

关键词： Capacitive deionization Fluoride La-MOFs Doping nitrogen

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Distributed Application Addressing in 6G Network

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China Communications 2024年第4期21卷 193-207页

作者： Liu Jie Chen Sibo Liu Yuqin Mo Zhiwei Lin Yilin Zhu Hongmei He Yufeng China Telecom Research institute Guangzhou 510632China

To ensure the extreme performances of the new 6G services,applications will be deployed at deep edge,resulting in a serious challenge of distributed application *** paper traces back the latest development of mobile network application addressing,analyzes two novel addressing methods in carrier network,and puts forward a 6G endogenous application addressing scheme by integrating some of their essence into the 6G network architecture,combining the new 6G capabilities of computing&network convergence,endogenous intelligence,and communication-sensing *** paper further illustrates how that the proposed method works in 6G networks and gives preliminary experimental verification.

关键词： application addressing CNC(Computing&Network Convergence) DNS(Domain Name System) ICN(Information-Centric Network) 6G

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FedDAA:a robust federated learning framework to protect privacy and defend against adversarial attack

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Frontiers of Computer Science 2024年第2期18卷 107-122页

作者： Shiwei LU Ruihu LI Wenbin LIU Fundamentals Department Air Force Engineering UniversityXi’an 710051China institute of Advanced Computational Science and Technology Guangzhou UniversityGuangzhou 510006China

Federated learning(FL)has emerged to break data-silo and protect clients’privacy in the field of artificial ***,deep leakage from gradient(DLG)attack can fully reconstruct clients’data from the submitted gradient,which threatens the fundamental privacy of *** cryptology and differential privacy prevent privacy leakage from gradient,they bring negative effect on communication overhead or model ***,the original distribution of local gradient has been changed in these schemes,which makes it difficult to defend against adversarial *** this paper,we propose a novel federated learning framework with model decomposition,aggregation and assembling(FedDAA),along with a training algorithm,to train federated model,where local gradient is decomposed into multiple blocks and sent to different proxy servers to complete *** bring better privacy protection performance to FedDAA,an indicator is designed based on image structural similarity to measure privacy leakage under DLG attack and an optimization method is given to protect privacy with the least proxy *** addition,we give defense schemes against adversarial attack in FedDAA and design an algorithm to verify the correctness of aggregated *** results demonstrate that FedDAA can reduce the structural similarity between the reconstructed image and the original image to 0.014 and remain model convergence accuracy as 0.952,thus having the best privacy protection performance and model training *** importantly,defense schemes against adversarial attack are compatible with privacy protection in FedDAA and the defense effects are not weaker than those in the traditional ***,verification algorithm of aggregation results brings about negligible overhead to FedDAA.

关键词： federated learning privacy protection adversarial attacks aggregated rule correctness verification

Estimating Critical Clearing Time of Grid Faults Using DA of State-Reduction Model of Power Systems

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 807-820页

作者： Yang Liu Zhongyang Chen Huanjin Yao Lin Yi Q.H.Wu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China China Southern Power Grid Research institute Guangzhou 510663China

This paper proposes a critical clearing time (CCT) estimation method by the domain of attraction (DA) of a state-reduction model of power systems using sum of squares (SOS) programming. By exploiting the property of the Jacobian matrix and the structure of the boundary of the DA, it is found the DA of the state-reduction model and that of the full model of a power system are topological isomorphism. There are one-to-one correspondence relationships between the number of equilibrium points, the type of equilibrium points, and solutions of the two system models. Based on these findings, an expanding interior algorithm is proposed with SOS programming to estimate the DA of the state-reduction model. State trajectories of the full model can be transformed to those of the state-reduction model by orthogonal or equiradius projection. In this way, CCT of a grid fault is estimated with the DA of the state-reduction model. The calculational burden of SOS programming in the DA estimation using the state-reduction model is rather small compared with using the full model. Simulation results show the proposed expanding interior algorithm is able to provide a tight estimation of DA of power systems with higher accuracy and lower time costs.

关键词： Critical clearing time domain of attraction expanding interior algorithm state-reduction model SOS programming

Enhanced Fenton-like process over Cu/L(+)-ascorbic acid co-doping mesoporous silica for toxicity reduction of emerging contaminants

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Frontiers of Environmental Science & Engineering 2024年第4期18卷 49-60页

作者： Yuhang Liu Wenxuan Deng Xiaojun Wu Chun Hu Lai Lyu Key Laboratory for Water Quality and Conservation of the Pearl River Delta(Ministry of Education) Institute of Environmental Research at Greater BayGuangzhou UniversityGuangzhou 510006China

Effective removal of emerging contaminants(ECs)to minimize their impacts on human health and the natural environment is a global *** the removal of ECs in water,we fabricated a seaweed spherical microsphere catalyst with Cu cation-πstructures by in situ doping of Cu species and ascorbic acid in mesoporous silica(Cu-C-MSNs)via a hydrothermal *** results indicate that bisphenol A(BPA)is substantially degraded within 5 min under natural conditions,with its biological toxicity considerably ***,industrial wastewater could also be effectively purified by Cu-C-MSNs/H_(2)O_(2) *** presence of metal sites and the complexation of ECs via cation-πinteraction andπ-πstacking on the catalyst surface were directly responsible for the polarization distribution of electrons,thus activating H_(2)O_(2) and dissolved oxygen(DO).The removal of contaminants could be attributed primarily to 1)the activation of H_(2)O_(2) into ^(·)OH to attack the contaminants and 2)self-cleavage because of the transfer of electrons from the contaminants to the *** study provides an innovative solution for the effective treatment of ECs and has positive implications for easing global environmental crises.

关键词： Cation-πstructures Polarization electric field Fenton-like process Contaminants cleavage