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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ding, Wenjun An, Ying Chen, Lixing Kan, Shichao Wu, Fan Qu, Zhe School of Computer Science and Engineering Central South University Changsha China Xiangjiang Laboratory Changsha China Big Data Institute Central South University Changsha China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China

ISBN: (纸本)157735897X

Federated Adversarial Learning (FAL) is a robust framework for resisting adversarial attacks on federated learning. Although some FAL studies have developed efficient algorithms, they primarily focus on convergence performance and overlook generalization. Generalization is crucial for evaluating algorithm performance on unseen data. However, generalization analysis is more challenging due to non-smooth adversarial loss functions. A common approach to addressing this issue is to leverage smoothness approximation. In this paper, we develop algorithm stability measures to evaluate the generalization performance of two popular FAL algorithms: Vanilla FAL (VFAL) and Slack FAL (SFAL), using three different smooth approximation methods: 1) Surrogate Smoothness Approximation (SSA), (2) Randomized Smoothness Approximation (RSA), and (3) Over-Parameterized Smoothness Approximation (OPSA). Based on our in-depth analysis, we answer how to properly set the smoothness approximation method to mitigate generalization error in FAL. Moreover, we identify RSA as the most effective generalization error reduction method. In highly data-heterogeneous scenarios, we also recommend employing SFAL to mitigate the deterioration of generalization performance caused by heterogeneity. Based on our theoretical results, we provide insights to help develop more efficient FAL algorithms, such as designing new metrics and dynamic aggregation rules to mitigate heterogeneity. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Fair and Verifiable Identity-Based Broadcast Proxy Re-Encryption With Designated Sender Feasible for Medical Internet of Things

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IEEE Transactions on information Forensics and security 2025年 20卷 6033-6045页

作者： Liqing Chen Shiyu Chen Hao Zhang Jian Weng Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an Jiangsu China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou Fujian China College of Information Science and Technology Jinan University Guangzhou Guangdong China

With the development of big data, cloud computing is widely used by individual and enterprise users due to its powerful computing and storage capabilities. How to share the encrypted data stored in cloud servers flexibly to a group of users and provide effective access control to the users is a hot topic in current research. For that, this paper proposes fair and verifiable identity-based broadcast proxy re-encryption with designated sender (FV-IBBPRE-DS) for Medical Internet of Things (MIoT), which gives the system model, formal definition, and security model of FV-IBBPRE-DS, and constructs a concrete FV-IBBPRE-DS scheme. The data owner in the FV-IBBPRE-DS scheme is able to designate a sender, which is given the decryption privilege of the encrypted data to a set of receivers. The scheme ensures that the participating entities perform the process of data sharing fairly and are able to verify the consistency of the ciphertexts. The FV-IBBPRE-DS scheme achieves chosen ciphertext attack security. The performance analysis demonstrates that the scheme in this paper has significant advantages in re-encrypted ciphertext generation and decryption phase, and can be applied to resource-constrained MIoT devices.

关键词： Receivers Encryption Hospitals Cryptography Cloud computing Genetics Servers Access control Sun Software engineering

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An Imperceptible Adversarial Attack Against 3-D Object Detectors in Autonomous Driving

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IEEE Internet of Things Journal 2025年第12期12卷 21404-21414页

作者： Wang, Yizhou Wu, Libing Jin, Jiong Wang, Enshu Zhang, Zhuangzhuang Zhao, Yu Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Swinburne University of Technology School of Engineering MelbourneVIC3122 Australia Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Hong Kong Department of Computer Science Hong Kong

As LiDAR-based 3-D object detection gains attention, existing research on point cloud adversarial attacks has exposed vulnerabilities in 3-D neural network models, which can further impact the reliability of perception systems in autonomous driving. However, current adversarial attacks primarily focus on the point cloud classification tasks, while detection tasks are more challenging to attack because they involve the localization of multiple targets and the sparse distribution of objects. Existing methods often implement attacks by adding global perturbations to the point clouds, which results in poor attack performance and lack of imperceptibility. In this article, we investigate the robustness of 3-D object detectors against adversarial examples. We propose a novel imperceptible attack method to generate 3-D adversarial point clouds. First, we introduce the saliency map for the point clouds, assigning the unique value to each point to measure its importance to the model’s discrimination results. These salient points are then aggregated and adaptively matched to different attack areas corresponding to different targets. Next, we design an optimization-based attack algorithm to generate adversarial point clouds, which are supervised by a dual-loss function consisting of the detection loss to ensure attack effectiveness and the distance loss to limit gap with the original point cloud. Finally, we conducted experiments on two real-world datasets, the KITTI and Waymo Open datasets, to evaluate the proposed attack method. Extensive experiments demonstrate that our attack method achieves average attack success rates of 83.07% and 77.11% on two datasets against seven 3-D object detectors with minimal perturbations. Additionally, the generated adversarial point clouds exhibit strong transferability across multiple mainstream detectors. © 2014 IEEE.

关键词： Object detection

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A New Joint Source-Channel Coding Scheme with Overlay Spread Spectrum Transmission 15

A New Joint Source-Channel Coding Scheme with Overlay Spread...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Chen, Yanzhi Liang, Jifan Wang, Qianfan Ma, Xiao School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou 510006 China

ISBN: (纸本)9798350324662

In this paper, we propose a new joint source-channel coding (JSCC) scheme, especially in conjunction with overlay spread spectrum transmission. At the transmitter, a source sequence is redescribed by classified enumerative (CE) coding, resulting in a two-stage description consisting of the index of its type-class and its rank in the associated type-class. The variable-length rank is encoded by a multiple-rate random code, while the fixed-length index of the type-class is encoded into a Walsh-Hadamard sequence, which is then overlaid (in the real field) on the coded signal corresponding to the index for transmission. In particular, to improve energy efficiency, the lower-rate component code is allocated with lower energy. At the receiver, the source can be decoded by searching for type-classes. To predict the performance of the proposed JSCC scheme, we present the estimated bound based on the performance of each type-class. Simulation results show that the performance matches well with the estimate, validating our analysis. Simulation results also show that the proposed JSCC scheme (without source statistics) can approach the JSCC bounds and outperform the double polar JSCC scheme (with perfect source statistics). © 2023 IEEE.

关键词： Direct-sequence spread spectrum (DSSS) enumerative coding joint source-channel coding (JSCC) locally constrained ordered statistic decoding (LC-OSD) random codes

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Improving Performance of LDPC-Coded Links via Partial Superposition Retransmission

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Journal of Communications and information networks 2022年第3期7卷 259-268页

作者： Yinchu Wang Qianfan Wang Ming Jiang Xiao Ma School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen UniversityGuangzhou 510006China School of Electronics and Information Technology Sun Yat-sen UniversityGuangzhou 510006China

In this paper,we propose a transmission scheme for uplink and downlink transmissions,where the fifth generation(5G)low-density parity-check(LDPC)codes are implemented for error *** the proposed scheme,the acknowledgment(ACK)or negative acknowledgment(NACK)feedback information is transmitted along with the payload data by cyclically shifting coded sequence,while the re-transmitted codewords are superimposed(XORed)partially on the current *** distinguished feature of the proposed transmission scheme is that it requires neither extra transmission bandwidth nor extra transmission *** also propose to truncate the error patterns for the purpose of reducing the implementation complexity and reducing the error *** results show that the proposed scheme significantly outperforms conventional LDPC-coded *** the 5G LDPC code with length 1920 at the signal-to-noise ratio(SNR)of 1.3 dB,the word error rate(WER)of the data transmitted by the proposed scheme is about 10^(−4),while that of the conventional LDPC-coded transmission is about 10^(−2).

关键词： block Markov superposition transmission(BMST) 5G LDPC codes free-ride coding hybrid automatic repeat request(HARQ) partial superposition

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A Revocable Pairing-Free Certificateless Signature Scheme Based on RSA Accumulator 23

A Revocable Pairing-Free Certificateless Signature Scheme Ba...

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23rd IEEE International Conference on Trust, security and Privacy in Computing and Communications, TrustCom 2024

作者： Shen, Zhuowei Kou, Xiao Yang, Taiyao Xu, Haoqin Wang, Dongbin Niu, Shaobo Southeast University School of Cyber Science and Engineering Nanjing China Minster of Education Key Laboratory of Computer Network and Information Intergration Nanjing China Beijing University of Post and Telecommunications School of Cyberspace Security Beijing China Ministry of Education Engineering Research Center of Blockchain and Network Convergence Technology Beijing China

Certificateless public key cryptography (CL-PKC) has garnered significant attention in recent years due to its ability to address the complex certificate management requirements inherent in public key infrastructure (PKI) systems and the key escrow issue associated with identity-based cryptography (IBC). However, the efficient revocation of illegitimate users in authentication schemes based on CL-PKC remains a challenge. Moreover, certain schemes still exhibit security vulnerabilities, such as susceptibility to key replacement attacks, and also require performance enhancements. In this article, we propose a novel certificateless signature scheme that incorporates the RSA accumulator. By leveraging the witness in the RSA accumulator to produce the public keys of users, the authenticity of the public key can be efficiently verified, thereby eliminating key replacement attacks. Additionally, by removing specific elements from the RSA accumulator and broadcasting the update credential to legitimate users via a public channel, our proposed scheme allows the effective revocation of malicious users. security analysis and performance evaluations indicate that our scheme provides comprehensive security and achieves high performance in terms of computational and communication costs in both signingverification and revocation processes. © 2024 IEEE.

关键词： Certificateless public key cryptography digital signature revocation RSA accumulator

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A Blockchain-based PHR Sharing Scheme with Attribute Privacy Protection

A Blockchain-based PHR Sharing Scheme with Attribute Privacy...

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

作者： Chaohe Lu Zhongyuan Yu Guijuan Wang Anming Dong Xiang Tian School of Computer Science Qufu Normal University Rizhao China School of Information Science and Engineering Lanzhou University Lanzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the rapid advancement and application of the Internet of Medical Things (IoMT), personal health records (PHRs) are now increasingly comprised of data collected by Internet of Things (IoT) devices and medical records documented by healthcare professionals. Personal health record (PHR) sharing demonstrates great potential in improving the accuracy of disease diagnosis. However, PHR sharing also brings risks such as illegal access and personal information leakage. Some works explored using blockchain or attribute-based encryption (ABE) to solve these privacy leakage problems, but those solutions did not pay attention to the user’s attribute privacy. In this work, we combine a linear secret sharing scheme (LSSS) and zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) scheme to design an efficient zero-knowledge proof protocol called zk-AHSNARK. It can verify the user’s attribute permissions while also hiding attribute information. Based on zk-AHSNARK, we propose a novel PHR sharing scheme that protects attribute privacy. Data security is ensured by storing encrypted data in the interplanetary file system (IPFS). In addition, we introduce keyword ciphertext search to achieve fast data retrieval, and we implement the search and verification algorithms via a smart contract, ensuring the trustworthiness and integrity of the execution. Finally, through a large number of simulations, we demonstrated the suggested scheme’s viability and security.

关键词： Access control Privacy Smart contracts Blockchains Encryption InterPlanetary File System Medical diagnosis Protection Secure storage Medical diagnostic imaging

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Age of information Analysis for CR-NOMA Aided Uplink Systems with Randomly Arrived Packets

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IEEE Transactions on Communications 2024年

作者： Sun, Yanshi Ye, Yanglin Ding, Zhiguo Zhou, Momiao Liu, Lei Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an710121 China

This paper studies the application of cognitive radio inspired non-orthogonal multiple access (CR-NOMA) to reduce age of information (AoI) for uplink transmission. In particular, a time division multiple access (TDMA) based legacy network is considered, where each user is allocated with a dedicated time slot to transmit its status update information. The CR-NOMA is implemented as an add-on to the TDMA legacy network, which enables each user to have more opportunities to transmit by sharing other user's time slots. A rigorous analytical framework is developed to obtain the expressions for AoIs achieved by CR-NOMA with and without re-transmission, by taking the randomness of the status update generating process into consideration. Numerical results are presented to verify the accuracy of the developed analysis. It is shown that the AoI can be significantly reduced by applying CR-NOMA compared to TDMA. Moreover, the use of re-transmission is helpful to reduce AoI, especially when the status arrival rate is low. © 1972-2012 IEEE.

关键词： Markov chains

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Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 15513-15526页

作者： Liu, Lei Feng, Jie Mu, Xuanyu Pei, Qingqi Lan, Dapeng Xiao, Ming Xidian University Guangzhou Institute of Technology Guangzhou510555 China Ministry of Education Shanghai201804 China Xidian University State Key Laboratory of Integrated Services Networks School of Telecommunication Engineering Shaanxi Xi'an710071 China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Shaanxi Xi'an710121 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China University of Oslo Department of Informatics Oslo0373 Norway Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm10044 Sweden

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading. © 2000-2011 IEEE.

关键词： Reinforcement learning

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Neural network Driven by Density and Parallel Features for Field-Road Mode Mining

Neural Network Driven by Density and Parallel Features for F...

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International Conference on computer Supported Cooperative Work in Design

作者： Fengqi Hao Xiyuan Zhao Cunxiang Bian Jinqiang Bai Qingyan Ding Faculty of Data Science City University of Macau Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Field-road mode mining (FRMM) has gained increasing attention because of its crucial role in machinery management. As the Global Navigation Satellite System (GNSS) is widely applied in agricultural machinery, many methods based on motion and spatial-temporal features of GNSS trajectory data have been proposed for FRMM. However, these methods ignore the density and parallel features of GNSS points. The two features are useful for FRMM because field points usually have a higher density and more parallel points than road points. Therefore, a neural network driven by density and parallel features is proposed for accurate FRMM. Firstly, a statistical method is designed to extract the density feature (i.e., the number of its neighbor points) and parallel feature (i.e., the number of approximately parallel points in its neighbors) of each point. Then, the two features and eight motion features (e.g., speed, direction, etc.) are fed into a neural network to extract valuable latent features. Finally, a linear classifier is used to identify the field and road categories of GNSS points based on the latent features. Experimental results show that our method outperforms state-of-the-art methods and achieves the accuracy of 91.69% and 86.44% on public Wheat and Paddy datasets, respectively.

关键词： Global navigation satellite system Accuracy Statistical analysis Federated learning Roads Neural networks Time series analysis Feature extraction Trajectory Data mining

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