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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Gan, Zeming Su, Linlin Li, Xianxian Wang, Jinyan Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin China Guangxi Normal University School of Computer Science and Engineering Guilin China

ISBN: (纸本)9798350330717

Extensive evidence shows that Graph Neural networks (GNNs) are vulnerable to adversarial attacks. Previous work has made great efforts to improve the performance and usability of GNNs, but they remain sensitive and vulnerable to local attacks. Local attacks slightly perturb the target nodes, resulting in misclassification of GNNs on these specific nodes. Graph Universal Defense (GUD) is proposed to generate a defense patch to protect important nodes in a graph from local attacks. However, the research on GUD is still in the preliminary stage. The unique patch does not adaptively protect different nodes, and it has weak robustness against strong local attacks. To address this problem, we propose Personalized Graph Universal Defense (PGUD). It maps nodes into several clusters, each corresponding to a personalized defense patch. We further instantiate a class-specific PGUD. From a perspective of effective perturbation at minimal cost, we theoretically derive the potential attacker nodes manipulated by the adversary, and use patches to mitigate their impact on target nodes. Experimental results show that our approach can be efficiently integrated into any GNN for downstream tasks and provide effective protection for different target nodes in a personalized manner. Our method significantly improves the robustness against local attacks compared to prior work and can be easily scaled to large graphs. © 2023 IEEE.

关键词： adversarial attack adversarial defense Graph neural networks robustness

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A Traceable Privacy-Preserving Transaction Protocol With Evolutionary Threshold Authentication

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IEEE Internet of Things Journal 2025年第12期12卷 20787-20799页

作者： Xie, Ninghai Li, Jiguo Lin, Chao Zhang, Yichen Shen, Jian Fujian Normal University College of Computer and Cyber Security Fuzhou350117 China School of Mathematics and Statistics Key Laboratory of Analytical Mathematics and Applications Ministry of Education Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Guangzhou University Guangdong Key Laboratory of Blockchain Security Guangzhou510006 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China

Decentralized payment systems, such as Bitcoin, enable immutable and transparent payments in a distributed manner. To address the issue of user privacy leakage due to transaction transparency, some efforts have enhanced privacy protection in decentralized payment systems. However, the lack of regulatory functions in these systems allows malicious users to engage in illegal activities. To balance user privacy protection and the regulation of malicious users, some efforts have attempted to introduce decentralized agencies. However, they have not considered the issue of agency corruption. In this paper, we propose a new traceable privacy-preserving transaction protocol, which tracks the addresses and transaction amounts of anonymous parties through decentralized institutions. To address the problem of committee corruption, we present a traceable privacy-preserving protocol with evolving threshold authentication based on a distributed random beacon (TPETA-to-DRB), enabling committee member updates. Furthermore, we prove that the protocol is secure under the random oracle model and conduct a comprehensive performance evaluation. © 2014 IEEE.

关键词： Authentication Protocol

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Relaxed Graph Semi-Supervised Contrastive Learning for Node Classification 29

Relaxed Graph Semi-Supervised Contrastive Learning for Node ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Li, Qiyu Li, Xianxian Qian, Haodong Li, De Wang, Jinyan Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin China Guangxi Normal University School of Computer Science and Engineering Guilin China

ISBN: (纸本)9798350330717

Graph Neural networks (GNNs) have emerged as promising tools in graph semi-supervised learning. They acquire low-dimensional node embeddings for downstream tasks by aggregating and updating features from neighboring nodes. However, in a semi-supervised setting, the availability of labeled node information is limited, resulting in the underutilization of vast amounts of unlabeled node information. Current research integrates contrastive learning with graph semi-supervised learning to better use this unlabeled node information. Nevertheless, the differences between cross-entropy and self-supervised contrastive loss are often overlooked. To address this issue, we propose a novel relaxed graph contrastive semi-supervised learning method. We analyze the difference between nodes correctly classified under cross-entropy loss and self-supervised contrastive loss. Inspired by this, we design a relaxed contrastive loss that considers the node features optimized under these two loss functions, thereby expanding the positive sample set. Furthermore, to ensure the quality of the positive sample set, we introduce a threshold constraint based on feature similarity and prediction results to select more reliable positive samples. The experimental results indicate that our method exhibits competitive performance across most datasets. © 2023 IEEE.

关键词： Contrastive learning Graph Neural networks Semi-supervised learning

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STGAT: A Spatio-Temporal Graph Attention network for Travel Demand Prediction

STGAT: A Spatio-Temporal Graph Attention Network for Travel ...

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International Conference on networking and network Applications (NaNA)

作者： Tao Yi Zhang YuBo Wang ZhiCheng Wei Key Lab of Network and Information Security College of Computer and Cyberspace Security Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Shijiazhuang Hebei Province China

Forecasting travel demand is a challenging task due to the complex spatial dependencies and dynamic temporal correlation of the traffic data. Furthermore, a limited representation of the given spatial graph structure may restrict the model's ability to effectively learn spatial-temporal dependencies. To extract latent semantic features from traffic data, this paper proposes a method to construct a traffic graph using the Markov cluster algorithm. The traffic semantic correlation matrix is constructed based on a traffic graph to obtain the deep semantic information. Specifically, this study proposes a Spatio-Temporal Graph Attention network(STGAT) for travel demand prediction. STGAT uses a graph attention layer based on the Node2Vec graph embedding algorithm, two convolutional layers based on the Markov cluster algorithm, and a long short-term memory network to capture spatial-temporal dependencies. Experimental results on the NYC Taxi dataset and the Chengdu online car dataset demonstrate that STGAT achieved state-of-the-art performance compared to other baseline models.

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Continuous Verification of Catastrophic Recalling in Machine Unlearning via Adversarial Testing 9

Continuous Verification of Catastrophic Recalling in Machine...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Qu, Y. Neil Ji, Zonghao Cui, Lei Zhang, Chong Liu, Lei Tian, Zhiyi Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250031 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250031 China Deakin University School of IT VIC3125 Australia Xidian University Guangzhou Institute of Technology Guangzhou510555 China University of Technology School of Computer Science SydneyNSW2007 Australia

ISBN: (纸本)9798350391367

Throughout the process of machine unlearning, in order to fulfill the requirements of machine unlearning and to comply with the rules of the right to be forgotten, it is natural to verify whether the unlearning model actually removes data points. Therefore, machine unlearning verification plays a crucial role. However, existing validation methods only perform one validation, and it is still challenging to determine whether subsequent updates of the model still maintain the unlearning effect. To address this challenge, we propose a novel continuous verification of catastrophic recalling in machine unlearning framework that aims to continuously evaluate whether a machine learning model effectively forgets specified data after an update. In this work, we first introduce adversarial testing as a crucial part of the overall framework. Verification then continues as the unlearned model is updated (adding or continuing to forget data points). We also analyzed the possibility that learning new data after unlearning may diminish the effect. Overall, our continuous unlearning verification framework provides a proactive approach to protecting individual privacy in the machine learning domain, thus facilitating the generation of more robust unlearning algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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A Deep Reinforcement Learning Approach for Online Taxi Dispatching

A Deep Reinforcement Learning Approach for Online Taxi Dispa...

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International Conference on networking and network Applications (NaNA)

作者： YuBo Wang TaoYi Zhang ZhiCheng Wei Key Lab of Network and Information Security College of Computer and Cyber Security Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Shijiazhuang Hebei Province China

With the development of smart city transportation systems, developing reasonable dispatching strategies for idle ride-hailing vehicles has become an urgent research problem. In this paper, we address the short-sightedness issue of existing dispatching strategies and design a ride-hailing dispatching model based on deep reinforcement learning in the Markov decision process(MDP) framework to obtain long-term dispatching strategies with foresight. Firstly, we define the basic framework of agent and optimize the dispatching strategy of ride-hailing services by defining a metric that combines instant revenue with the demand situation of the next state in the action space as a reward function. Then, we construct an order-matching system simulator based on historical order data sets to facilitate interaction between the intelligent agent and the environment. Finally, based on the Bellman optimality equation, we approximate the value function using the Eval Net and Target Net, and train the model using Temporal Difference Learning. Simulation experiments demonstrate that this approach can increase driver income, provide more services to passengers, and effectively improve the operating efficiency of the transportation network.

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Research on the Design of Government Data Governance Evaluation System under the Perspective of Digital Government 9

Research on the Design of Government Data Governance Evaluat...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Xu, Zirui Sun, Jinyang Lu, Zhe Han, Delong Qilu University of Technology Shandong Academy of Sciences Information Research Institute Shandong Academy of Sciences Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd. Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

In the context of the digital era, government data governance is a key factor in promoting the modernization of government governance, but there is a lack of a complete set of evaluation index system to standardize the content and scope of government data governance. This paper adopts the policy tool theory to code and analyze the 12 policies from the three policy tool dimensions of supply layer, demand layer, and environment layer, and proposes three first-level evaluation indicators of basic environment, actual effectiveness, and safeguard mechanism, on the basis of which the evaluation content is selected in a focused manner based on the integration of policy documents related to data governance. Meanwhile, a comprehensive, scientific and objective evaluation system of government data governance is constructed by adopting assessment means combining subjectivity and objectivity, and quantitative and qualitative. The system is designed to assess and guide the data governance practices of the domestic government, point out the direction for government departments at all levels to carry out data governance, and play an important role in promoting the transparency and efficiency of data management. © 2024 IEEE.

关键词： data governance digital government government information indicator system policy tools

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Distributed Deep Learning for Cooperative Computation Offloading in Low Earth Orbit Satellite networks

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China Communications 2022年第4期19卷 230-243页

作者： Qingqing Tang Zesong Fei Bin Li School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsMinistry of EducationNanjing 210003China

Low earth orbit(LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the“ubiquitous connection” of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading(DDLCCO) algorithm, where multiple parallel deep neural networks(DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.

关键词： LEO satellite networks computation offloading deep neural networks

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Multi-Relational Variational Contrastive Learning for Next POI Recommendation

Multi-Relational Variational Contrastive Learning for Next P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lin Chen Peipei Wang Xiaohui Han Lijuan Xu 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 Quan Cheng Laboratory Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Next point-of-interest (POI) recommendation aims to predict the next interested POI to the user based on their historical check-in data in location-based social services. Most existing studies have attempted to model user visiting behaviors via sequence-based and graph-based models, and have achieved impressive performance. However, there is still room to explore the implicit transition preferences and contextual multiple semantic relationships among various POIs. To this end, we propose a novel graph-based Multi-Relational Variational Contrastive Learning (MRVCL) method for next POI recommendation, which captures local similarity associations and global contextual dependencies among POIs. Specifically, an order-free local-relational adaptive weighting module is designed to alleviate the problem of insufficient utilization of multi-hop neighbor information caused by the limit of neighbor order of nodes. We then develop a contextaware global-relational encoding module to capture implicit semantic sequential relationships. Finally, generative variational-contrastive learning is employed to construct a continuous representation of the latent features and reinforce the quality of representation learning. Extensive experiments on two real-world datasets validate that our MRVCL outperforms existing state-of-the-art methods on various evaluation metrics. To facilitate future research, our code and data are open-sourced at https://***/LinCH-en/MRVCL.

关键词： Representation learning Measurement Correlation Semantics Neural networks Contrastive learning Signal processing Transformers Encoding Speech processing

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NeuVSA: A Unified and Efficient Accelerator for Neural Vector Search 31

NeuVSA: A Unified and Efficient Accelerator for Neural Vecto...

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Yuan, Ziming Dai, Lei Li, Wen Zhang, Jie Liang, Shengwen Wang, Ying Liu, Cheng Li, Huawei Li, Xiaowei Guo, Jiafeng Wang, Peng Chen, Renhai Zhang, Gong Shanxi University School of Computer and Information Technology China Peking University School of Computer Science China Institute of Computing Technology CAS State Key Lab of Processors China University of Chinese Academy of Sciences China Zhongguancun National Laboratory Beijing China Institute of Computing Technology CAS Key Lab of Network Data Science and Technology China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9798331506476

Neural Vector Search (NVS) has exhibited superior search quality over traditional key-based strategies for information retrieval tasks. An effective NVS architecture requires high recall, low latency, and high throughput to enhance user experience and cost-efficiency. However, implementing NVS on existing neural network accelerators and vector search accelerators is sub-optimal due to the separation between the embedding stage and vector search stage at both algorithm and architecture levels. Fortunately, we unveil that Product Quantization (PQ) opens up an opportunity to break separation. However, existing PQ algorithms and accelerators still focus on either the embedding stage or the vector search stage, rather than both simultaneously. Simply combining existing solutions still follows the beaten track of separation and suffers from insufficient parallelization, frequent data access conflicts, and the absence of scheduling, thus failing to reach optimal recall, latency, and throughput. To this end, we propose a unified and efficient NVS accelerator dubbed NeuVSA based on algorithm and architecture co-design philosophy. Specifically, on the algorithm level, we propose a learned PQ-based unified NVS algorithm that consolidates two separate stages into the same computing and memory access paradigm. It integrates an end-to-end joint training strategy to learn the optimal codebook and index for enhanced recall and reduced PQ complexity, thus achieving smoother acceleration. On the architecture level, we customize a homogeneous NVS accelerator based on the unified NVS algorithm. Each sub-accelerator is optimized to exploit all parallelism exposed by unified NVS, incorporating a structured index assignment strategy and an elastic on-chip buffer to alleviate buffer conflicts for reduced latency. All sub-accelerators are coordinated using a hardware-aware scheduling strategy for boosted throughput. Experimental results show that the joint training strategy improves recall

关键词： network embeddings

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