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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Li, Qiaoman Gao, Xiaoying Meng, Wenyang Ma, Jianbin Hebei Agricultural University College of Information Science and Technology Baoding China Victoria University of Wellington School of Engineering and Computer Science Wellington New Zealand College of Information Science and Technology Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural University Baoding China

ISBN: (纸本)9798400704956

Classification for high-dimensional data is a challenging task due to a great number of redundant and irrelevant features. Genetic Programming (GP) has its built-in feature selection characteristics and is suitable for processing high-dimensional data. However, if the number of features in GP individual is not restricted, the bloat phenomenon will occur, which affects the generalization of the training model. Moreover, GP is easy to fall into local search in the evolutionary process. In this paper, a multi-objective multi-population GP classifier construction method is proposed, which uses a multipopulation co-evolution strategy to divide the evolving population into the main and auxiliary populations with different evolutionary strategies and different evaluation criteria, and employs a multiobjective strategy to ensure classification performance and restrict the number of features at the same time. Experiments on seven high-dimensional datasets show that our proposed multi-population co-evolution strategy and multi-objective strategy are all effective to improve the classification performance of GP classifiers. Comparisons with three state-of-the-art GP classifier construction methods show that our proposed methods achieve better or comparable classification performance on most cases. © 2024 held by the owner/author(s).

关键词： Spatio-temporal data

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A Service-Oriented Anonymous Authentication Scheme with Partial-Disclosure in C-ITS

A Service-Oriented Anonymous Authentication Scheme with Part...

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International Conference on Networking and Network Applications (NaNA)

作者： Junbo Huang Youliang Tian Kedi Yang Linjie Wang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Data Science Tongren University Tongren China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

Service-oriented communication in collaborative intelligent transportation systems (C-ITS) can provide users with a variety of services to enhance the driving experience. Implementing anonymous authentication in service-oriented communication can protect users’ privacy while obtaining value-added services. However, existing service-oriented anonymous authentication schemes are either only applicable to single-service, or do not fully consider how to achieve partial-disclosure of credentials in multi-service. In this paper, we propose an anonymous authentication scheme with partial-disclosure through attribute-based credentials. Firstly, Service qualifications are defined as a set of attributes, and service providers sign on the attributes to generate credentials for users. Our scheme supports credential aggregation and takes into account both single-service and multiservice scenarios. Secondly, through the feature of redactable signature, users can achieve partial-disclosure on aggregated credential. Finally, it is proved that our scheme can ensure the efficiency of authentication while ensuring security, which is of practical significance.

关键词： Privacy Authentication Collaboration Security Intelligent transportation systems Qualifications

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Feature Decoupled of Deep Mutual Information Maximization 2

Feature Decoupled of Deep Mutual Information Maximization

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2nd International Conference on Automation, Robotics and computer Engineering, ICARCE 2023

作者： He, Xing Peng, Changgen Wang, Lin Tan, Weijie Wang, Zifan State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China Guizhou Big Data Academy Guizhou University Guiyang China Guizhou Minzu University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang China Institute of Guizhou Aerospace Measuring and Testing Technology Guiyang China

ISBN: (纸本)9798350308341

In deep learning, supervised learning techniques usually require a large amount of expensive labeled data to train the network, and the feature representations extracted by the model usually mix multiple attributes, resulting in feature representations that are difficult to decouple and are non-interpretable, which restricts the application and development of deep learning techniques, and for this reason, it is particularly important to study decoupled feature representation methods for unsupervised learning. Although the Learning deep representations by mutual information estimation and maximization (DIM) method achieves excellent results in unsupervised learning, the feature representations learned by the DIM method still suffer from the problem of difficult decoupling. decoupling problem. To address this problem, we minimize the mutual information between the intermediate layer feature representations learned by the hidden layer of the encoder during the encoder training process, so that the features learned by each filter are as uncorrelated as possible, thus realizing feature decoupling, and our method is called FP-DIM. Finally, the effectiveness of the FP-DIM method is verified on the CIFAR-10, STL-10, and Fashion-MNIST datasets. The experiments show that our proposed FP-DIM method is more significant for the learned decouplable middle layer feature representation. Finally, we also propose a reflection of future research for the FP-DIM method, aiming to provide a research idea and direction for solving unsupervised interpretable machine learning and to lay a solid theoretical and application foundation for machine learning fields such as image classification and migration learning. © 2023 IEEE.

关键词： Unsupervised learning

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SAD: Website Fingerprinting Defense Based on Adversarial Examples 1

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4th EAI International Conference on Security and Privacy in New Computing Environments, SPNCE 2021

作者： Tang, Renzhi Shen, Guowei Guo, Chun Cui, Yunhe College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Key Laboratory of Public Big Data Guiyang550025 China

ISBN: (数字)9783030967918

ISBN: (纸本)9783030967901

Website Fingerprinting (WF) attacks can infer website names from encrypted network traffic when the victim is browsing the website. Inherent defenses of anonymous communication systems such as The Onion Router (Tor) cannot compete with current WF attacks. The state-of-the-art attack based on deep learning can gain over 98% accuracy in Tor. However, the existing defense will bring high bandwidth overhead, affect the user’s network experience or cannot be used in actual scenarios. Some researchers found that deep learning models are vulnerable to adversarial examples. In this paper, based on adversarial examples we propose Segmented Adversary Defense (SAD) for deep learning-based WF attacks. Network traffic is divided into multiple segments. Then, the adversarial examples for each segment of traffic can be generated by SAD. Finally, dummy packets from adversarial examples are inserted after each segment traffic. Experimentally, our results show that SAD can effectively reduce the accuracy of WF attacks. The technique drops the accuracy of the state-of-the-art attack hardened from 96% to 3% while incurring only 40% bandwidth overhead. Compared with the existing proposed defense named Deep Fingerprinting Defender (DFD), the defense effect of SAD is better under the same bandwidth overhead. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Websites

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Communication-Efficient Federated Learning with Sparsity and Quantization

Communication-Efficient Federated Learning with Sparsity and...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Zhong Long Yuling Chen Hui Dou Yun Luo Chaoyue Tan Yancheng Sun State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

Google proposed federated learning in 2016, which aims to solve the problems of data silos and privacy leakage in machine learning. However, as the difficulty of the target task increases and the model performance requirements progressively improve, these have to consume more communication costs to increase the model parameters. In order to solve this problem, this paper proposes the Federated Learning with Sparsity and Quantization(FedSQ) method, where we select the gradients with a large rate of change in the gradients of the participants in each round for transmission, and utilize binary compression to quantize the selected model parameters, while adopting a suitable aggregation algorithm to replace the original FedAvg, so that it does not excessively impair the model accuracy in the case of high-fold compression. Finally, we verify the effectiveness of the scheme proposed in this paper, which can improve the quality of model training under model compression.

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A Write-Optimized PM-oriented B+-tree with Aligned Flush and Selective Migration

A Write-Optimized PM-oriented B+-tree with Aligned Flush and...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Xianyu He Runyu Zhang Pengpeng Tian Lening Zhou Min Lian Chaoshu Yang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

Emerging Persistent Memories (PMs) usually have the severe drawback of expensive writes. Therefore, existing PM-oriented $\mathrm{B}^{+} -$trees mainly concentrate on alleviating the write overhead (i.e., reducing the writes to PM and the flush instructions). Unfortunately, these schemes still employ improper data organization in leaf nodes, which can lead to massive data migration during insertions or node splits. This paper proposes a write-optimized PM-oriented $\mathrm{B}^{+} -$tree (WPB$^{+} -$tree) to solve the above problems. WPB$^{+} -$tree first adopts a buffer-assisted mechanism that temporarily stores the newly inserted data to reduce the overhead of data migration and flush. Second, WPB$^{+}$ tree employs a selective migration of node entries to achieve less data migration when splitting a node. We implement the proposed WPB$^{+} -$tree with actual persistent memory. Compared with state-of-the-art solutions (i.e., FAST&FAIR and SSB-Tree), WPB$^{+} -$tree achieves up to 17%, 40%, and 20% higher throughput in the insert, search, and scan operations, respectively.

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A Blockchain of Blockchains Structure Based on Asset Trading Scheme

A Blockchain of Blockchains Structure Based on Asset Trading...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Siyuan Huang Yuling Chen Chaoyue Tan Hui Dou Yun Luo Yuxiang Yang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

In the realm of blockchain applications, the issue of value isolation, commonly referred to as the value island problem, imposes inherent limitations on the applicability of corresponding assets. To overcome this challenge, researchers have dedicated their efforts to exploring diverse solutions for facilitating cross-chain transactions involving various assets. These solutions include centralized exchanges and hash time lock protocols. However, these approaches are subject to significant limitations. Centralized exchanges, while providing transactional functionalities, undermine the decentralized nature intrinsic to blockchain technology. Conversely, hash time lock protocols necessitate a high level of trust between transacting parties. Furthermore, although decentralized exchanges have emerged to support atomic cross-chain exchanges, their transactional architectures are restricted to either a bilateral relationship between two blockchains or a subset of pre-established blockchains. Therefore, the construction of a non-trust-based, decentralized exchange that supports multiple blockchain networks remains an ongoing challenge. In this research, we propose Vchain, a cross-chain relay transaction scheme, designed with a BoBs(Blockchain of Blockchains) structure, to address the aforementioned challenges. Vchain enables the participation of various accounts within blockchain ecosystems that support smart contract functionalities. It ensures the security and integrity of user transactions by leveraging third-party cryptocurrency assets as collateral. Moreover, users have the flexibility to selectively choose transaction counterparties based on the quantity of collateral assets provided, thereby maximizing their transactional interests. Consequently, Vchain enables non-trust-based, multi-chain asset cross-chain transactions, addressing the limitations of existing solutions.

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Power Optimization for Low Transmission Delay in Software Defined data Center Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5716-5730页

作者： Zhao, Yong Cao, Jiannong Wang, Xingwei Li, Fuliang He, Qiang Liu, Xiaojie University of Electronic Science and Technology of China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Laboratory of Intelligent Collaborative Computing Chengdu611731 China Hong Kong Polytechnic University Department of Computing Hung Hom Hong Kong Northeastern University College of Computer Science and Engineering Shenyang110819 China Northeastern University College of Medicine and Biological Information Engineering Shenyang110819 China Pengcheng Laboratory Shenzhen518066 China

Software-Defined data Center Networks (SDDCNs) utilizes Software Defined Networking (SDN) as a network architecture to achieve highly flexible, programmable, and automated management of data Center Networks (DCNs). The high energy consumption of DCNs remains a persistent and significant challenge. Thus, the energy saving is crucial and imperative for DCNs. Current energy-efficient solutions primarily rely on flow consolidation and dynamic device sleeping techniques to reduce energy consumption. However, these approaches often yield long Flow Completion Time (FCT), potentially resulting in violations of service-level agreements, particularly for delay-sensitive applications. In this paper, we formulate the problem of minimizing the power consumption in SDDCNs as key objective while ensuring timely FCT for delay-sensitive applications. To solve this problem, we first introduce the Active Network Generation (ANG) approach, which generates a minimal active subnet with the least number of active devices while meeting the current traffic demand. Subsequently, we propose two algorithms based on the type of applications: the Delay-Tolerant Flow Route (DTFR) algorithm for delay-tolerant applications and the Delay-Sensitive Flow Route (DSFR) algorithm for delay-sensitive applications. Simulation results demonstrate that our propose solution achieves an energy-saving rate of up to 67.77% and significantly reduces FCT compared to benchmark solutions. © 2002-2012 IEEE.

关键词： Energy efficiency

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Learning a Mini-Batch Graph Transformer via Two-Stage Interaction Augmentation 27

Learning a Mini-Batch Graph Transformer via Two-Stage Intera...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Li, Wenda Chen, Kaixuan Liu, Shunyu Zheng, Tongya Huang, Wenjie Song, Mingli State Key Laboratory of Blockchain and Security Zhejiang University China School of Software Technology Zhejiang University China Institute of Blockchain and Data Security China Big Graph Center School of Computer and Computing Science Hangzhou City University China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781643685489

Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robustness. However, existing methods for processing local information either rely on sampling or simple aggregation, which respectively result in the loss and squashing of critical neighbor information. Moreover, the limited number of nodes in each mini-batch restricts the model’s capacity to capture the global characteristic of the graph. In this paper, we propose LGMformer, a novel MGT model that employs a two-stage augmented interaction strategy, transitioning from local to global perspectives, to address the aforementioned bottlenecks. The local interaction augmentation (LIA) presents a neighbor-target interaction Transformer (NTIformer) to acquire an insightful understanding of the co-interaction patterns between neighbors and the target node, resulting in a locally effective token list that serves as input for the MGT. In contrast, global interaction augmentation (GIA) adopts a cross-attention mechanism to incorporate entire graph prototypes into the target node representation, thereby compensating for the global graph information to ensure a more comprehensive perception. To this end, LGMformer achieves the enhancement of node representations under the MGT paradigm. Experimental results related to node classification on the ten benchmark datasets demonstrate the effectiveness of the proposed method. Our code is available at https://***/l-wd/LGMformer. © 2024 The Authors.

关键词： Contrastive Learning

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A High Capacity Combined Carrier Steganography Without Embedding Image

A High Capacity Combined Carrier Steganography Without Embed...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Yangwen Zhang Yuling Chen Hui Dou Yun Luo Chaoyue Tan Maolin Xiong State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

The technique of image steganography is the embedding of secret information into the carrier image by means of algorithms that are widely used in the field of information transmission, but steganalysis tools built with traditional steganographic algorithms can easily identify them. The technique of steganography without embedding (SWE) is the mapping of the secret information onto the noise and the generation of an image by mapping the noise of the secret information. This SWE technique is difficult to find without directly using secret information. But the capacity of secret information is less and the extraction accuracy is low when the SWE is currently hidden. Therefore, a steganography without embedding technique for carrier secret information is proposed in this paper based on the above problems. Initially, the secret information is mapped into the carrier noise, which can increase the secret information capacity of steganography and improve the accuracy of extracting information. Finally, the effectiveness and generality of the design method will be verified by experiments.

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