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Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

Digital Communications and networks 2025年第2期11卷 442-454页

作者： Zhou Zhou Youliang Tian Jinbo Xiong Changgen Peng Jing Li Nan Yang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou 350117China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin 541004China

Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog ***,existing studies focus on centralized single-layer aggregation federated learning architecture,which lack the consideration of cross-domain and asynchronous robustness of federated learning,and rarely integrate verification mechanisms from the perspective of *** address the above challenges,we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL)framework based on dual aggregation for cross-domain *** particular,we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between ***,we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models'availability of intra-domain ***,we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using ***,security analysis demonstrates the security and privacy effectiveness of BSAFL,and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.

关键词： Blockchain Signcryption Federated learning Asynchronous Fog computing

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A New Edge Perturbation Mechanism for Privacy-Preserving Data Collection in IOT

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Chinese Journal of Electronics 2023年第3期32卷 603-612页

作者： CHEN Qiuling YE Ayong ZHANG Qiang HUANG Chuan College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology

A growing amount of data containing the sensitive information of users is being collected by emerging smart connected devices to the center server in Internet of things(IoT) era, which raises serious privacy concerns for millions of users. However, existing perturbation methods are not effective because of increased disclosure risk and reduced data utility, especially for small data sets. To overcome this issue, we propose a new edge perturbation mechanism based on the concept of global sensitivity to protect the sensitive information in IoT data collection. The edge server is used to mask users' sensitive data, which can not only avoid the data leakage caused by centralized perturbation, but also achieve better data utility than local perturbation. In addition, we present a global noise generation algorithm based on edge perturbation. Each edge server utilizes the global noise generated by the center server to perturb users' sensitive data. It can minimize the disclosure risk while ensuring that the results of commonly performed statistical analyses are identical and equal for both the raw and the perturbed data. Finally, theoretical and experimental evaluations indicate that the proposed mechanism is private and accurate for small data sets.

关键词： Data privacy Sensitivity Statistical analysis Perturbation methods Data collection Data models Servers

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Pilot Backdoor Attack against Deep Reinforcement Learning Empowered intelligent Reflection Surface for Smart Radio

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4891-4903页

作者： Huang, Yunsong Wang, Hui-Ming Wang, Zhaowei Liu, Weicheng Xi’an Jiaotong University School of Information and Communications Engineering Xi’an710049 China Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China

intelligent reflection surface (IRS) has been used to assist communication by reflection and beamforming where a direct path is not available. Thus, IRS adjusts the wireless channel to enhance the data transmission efficiency with low power consumption. Recently, deep reinforcement learning (DRL) has been exploited in IRS coefficients optimization. IRS can be controlled by DRL to adapt their phase shift to the propagation environment and an expected reflection pattern can be obtained. However, due to the openness of wireless channel and the unexplainability of DRL, it is vulnerable to adversary attacks lauched through wireless channels. In this paper, we investigate a pilot contamination based backdoor attack against DRL based IRS beamforming, where an IRS controlled by an adversary attacker is used to contaminate the channel state information (CSI) during the training phase. The backdoor attack is covert because the adversary need not to know the legitimate pilot sequence and DRL performs well when the adversary IRS keeps inactive. We show that the backdoor attack can reduce the data rate significantly with the adversary IRS. At last, we propose a retraining method agaist the attack to recover the data rate. © 2025 IEEE. All rights reserved.

关键词： Deep reinforcement learning

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An Enhanced Clustering-Based (k, t)-Anonymity Algorithm for Graphs

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Chinese Journal of Electronics 2025年第1期34卷 365-372页

作者： Yuanyuan Wang Xing Zhang Zhiguang Chu Wei Shi Xiang Li School of Electronics and Information Engineering Liaoning University of Technology Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Faculty of Information Technology Beijing University of Technology

As people become increasingly reliant on the Internet, securely storing and publishing private data has become an important issue. In real life, the release of graph data can lead to privacy breaches, which is a highly challenging problem. Although current research has addressed the issue of identity disclosure, there are still two challenges: First, the privacy protection for large-scale datasets is not yet comprehensive; Second, it is difficult to simultaneously protect the privacy of nodes, edges, and attributes in social networks. To address these issues, this paper proposes a(k,t)-graph anonymity algorithm based on enhanced clustering. The algorithm uses k-means++ clustering for k-anonymity and t-closeness to improve k-anonymity. We evaluate the privacy and efficiency of this method on two datasets and achieved good results. This research is of great significance for addressing the problem of privacy breaches that may arise from the publication of graph data.

关键词： Data privacy Uncertainty Social networking (online) Publishing Heuristic algorithms Scalability Clustering algorithms Privacy breach Internet Protection

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A new quantum key distribution resource allocation and routing optimization scheme

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Chinese Physics B 2024年第3期33卷 244-259页

作者：毕琳袁晓同吴炜杰林升熙 School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130012China Key Laboratory of Network and Information Security in Jilin Province Changchun 130012China

Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic ***,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical *** this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet ***,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot ***,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key ***,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key *** results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.

关键词： quantum key distribution(QKD) resource allocation key storage routing algorithm

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TMC-GCN: Encrypted Traffic Mapping Classification Method Based on Graph Convolutional networks

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Computers, Materials & Continua 2025年第2期82卷 3179-3201页

作者： Baoquan Liu Xi Chen Qingjun Yuan Degang Li Chunxiang Gu School of Cyberspace Security Zhengzhou UniversityZhengzhou450002China School of Cyberspace Security Information Engineering UniversityZhengzhou450001China Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001China

With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.

关键词： Encrypted traffic classification deep learning graph neural networks multi-layer perceptron graph convolutional networks

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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

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Digital Communications and networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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High capacity reversible data hiding in encrypted images based on adaptive quadtree partitioning and MSB prediction

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Frontiers of Information Technology & Electronic Engineering 2023年第8期24卷 1156-1168页

作者： Kaili QI Minqing ZHANG Fuqiang DI Yongjun KONG Key Laboratory of Network&Information Security under Chinese People Armed Police Force Engineering University of PAPXi’an710086China

To improve the embedding capacity of reversible data hiding in encrypted images(RDH-EI),a new RDH-EI scheme is proposed based on adaptive quadtree partitioning and most significant bit(MSB)***,according to the smoothness of the image,the image is partitioned into blocks based on adaptive quadtree partitioning,and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted *** the data embedding stage,the adaptive MSB prediction method proposed by Wang and He(2022)is improved by taking the upper-left pixel in the block as the target pixel,to predict other pixels to free up more embedding *** the best of our knowledge,quadtree partitioning is first applied to *** results show that the proposed method is reversible and separable,and that its average embedding capacity is *** gray images with a size of 512×512,the average embedding capacity is increased by 25565 *** all smooth images with improved embedding capacity,the average embedding capacity is increased by about 35530 bits.

关键词： Adaptive quadtree partitioning Adaptive most significant bit(MSB)prediction Reversible data hiding in encrypted images(RDH-EI) High embedding capacity

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Threat Model and Defense Scheme for Side-Channel Attacks in Client-Side Deduplication

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Tsinghua Science and Technology 2023年第1期28卷 1-12页

作者： Guanxiong Ha Hang Chen Chunfu Jia Mingyue Li College of Cyber Science Nankai UniversityTianjin 300350China Tianjin Key Laboratory of Network and Data Security Technology Tianjin 300350China

In cloud storage,client-side deduplication is widely used to reduce storage and communication *** client-side deduplication,if the cloud server detects that the user’s outsourced data have been stored,then clients will not need to reupload the ***,the information on whether data need to be uploaded can be used as a side-channel,which can consequently be exploited by adversaries to compromise data *** this paper,we propose a new threat model against side-channel *** from existing schemes,the adversary could learn the approximate ratio of stored chunks to unstored chunks in outsourced files,and this ratio will affect the probability that the adversary compromises the data privacy through side-channel *** this threat model,we design two defense schemes to minimize privacy leakage,both of which design interaction protocols between clients and the server during deduplication checks to reduce the probability that the adversary compromises data *** analyze the security of our schemes,and evaluate their performances based on a real-world *** with existing schemes,our schemes can better mitigate data privacy leakage and have a slightly lower communication cost.

关键词： cloud storage deduplication side-channel privacy

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Reversible Data Hiding Algorithm in Encrypted Images Based on Adaptive Median Edge Detection and Ciphertext-Policy Attribute-Based Encryption

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Computers, Materials & Continua 2024年第10期81卷 1123-1155页

作者： Zongbao Jiang Minqing Zhang Weina Dong Chao Jiang Fuqiang Di Key Laboratory of Network and Information Security of People’s Armed Police Chinese People’s Armed Police Force Engineering UniversityXi’an710086China

With the rapid advancement of cloud computing technology,reversible data hiding algorithms in encrypted images(RDH-EI)have developed into an important field of study concentrated on safeguarding privacy in distributed cloud ***,existing algorithms often suffer from low embedding capacities and are inadequate for complex data access *** address these challenges,this paper proposes a novel reversible data hiding algorithm in encrypted images based on adaptive median edge detection(AMED)and ciphertext-policy attributebased encryption(CP-ABE).This proposed algorithm enhances the conventional median edge detection(MED)by incorporating dynamic variables to improve pixel prediction *** carrier image is subsequently reconstructed using the Huffman coding *** image generation is then achieved by encrypting the image based on system user attributes and data access rights,with the hierarchical embedding of the group’s secret data seamlessly integrated during the encryption process using the CP-ABE ***,the encrypted image is transmitted to the data hider,enabling independent embedding of the secret data and resulting in the creation of the marked encrypted *** approach allows only the receiver to extract the authorized group’s secret data,thereby enabling fine-grained,controlled *** results indicate that,in contrast to current algorithms,the method introduced here considerably improves the embedding rate while preserving lossless image ***,the average maximum embedding rates for the(3,4)-threshold and(6,6)-threshold schemes reach 5.7853 bits per pixel(bpp)and 7.7781 bpp,respectively,across the BOSSbase,BOW-2,and USD ***,the algorithm facilitates permission-granting and joint-decryption ***,this paper conducts a comprehensive examination of the algorithm’s robustness using metrics such as image correlation,information entropy,and number of pix

关键词： Ciphertext-policy attribute-based encryption complex data access structure reversible data hiding large embedding space

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