检索结果-内蒙古大学图书馆

Privacy-Preserving Federated Learning Based on dataset Condensation

IEEE Transactions on Consumer Electronics 2024年

作者： Zhang, Di Le, Junqing Mu, Nankun Liu, Gao Xiang, Tao Chongqing University College of Computer Science Chongqing401331 China Ministry of Education Key Laboratory of Data Protection and Intelligent Management Sichuan University Sichuan610065 China

Federated Learning (FL) is a decentralized learning framework that facilitates learning from large-scale datasets while keeping raw data locally. It dismantles data silos and addresses privacy concerns tied to direct data access. However, the substantial interaction of model parameters between local nodes and servers in FL significantly impacts training efficiency. Moreover, inversion attacks on model weights may also lead to serious privacy leakage. Towards this end, we combine data condensation and differential privacy (DP) techniques, designing a communication-efficient and privacy-preserving FL scheme named PPFL-DC. Specifically, the designed DP-processing method protects the model weights trained on the real dataset. Then, the protected model weights are used to guide the generation of lightweight and privacy-preserving synthetic images, where an efficient synthetic data generation method with an appropriate number of iterations is designed to reduce local computation costs. Instead of large-sized model weights, these small synthetic images are sent to the server. Subsequently, the server recovers the model weights from the synthetic images and uses them for model aggregation to achieve highly accurate global model updates. PPFL-DC effectively reduces communication and computation overhead, ensures client-level privacy protection, and maintains a high global model accuracy, even when dealing with Non-IID datasets. Finally, the theoretical analysis and experimental results on three benchmark datasets demonstrate the superiority of PPFL-DC in performance over the previous FL schemes. © 1975-2011 IEEE.

关键词： data condensation differential privacy efficient communication federated learning Privacy protection

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Reinforcement Learning Methods for Fixed-Wing Aircraft Control 9

Reinforcement Learning Methods for Fixed-Wing Aircraft Contr...

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Li, Lun Zhang, Xuebo Wang, Yang Qian, Chenxu Zhao, Minghui Nankai University Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Tianjin China Tianjin Municipal Data Management Center Tianjin China

ISBN: (纸本)9798350383706

In recent years, reinforcement learning (RL) has been widely researched for fixed-wing aircraft control, offering transformative potential for more adaptive, efficient, and autonomous flight operations. This paper presents a comprehensive analysis of the current state, advantages, challenges, and future prospects of employing RL in fixed-wing aircraft control. The advantages of RL include model-free, adaptive decision-making, and effectively solving complex nonlinear constraints, leading to increased efficiency and maneuverability in high-dynamic environments. However, significant challenges, such as the guarantee of stability and optimization, interpretability, and sim-to-real transferring, highlight areas necessitating further attention and development. Overcoming these challenges requires collaborative efforts among aviation experts, control theory experts, and machine learning researchers to ensure safe, reliable integration of RL in flight systems. As research and advancements continue, the RL holds promise for providing a more efficient, maneuverable, and autonomous strategy for controlling fixed-wing aircraft in the future. © 2023 IEEE.

关键词： Reinforcement learning

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Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

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Computer Modeling in Engineering & Sciences 2023年第4期135卷 653-671页

作者： Xuhui Zhu Pingfan Xia Qizhi He Zhiwei Ni Liping Ni School of Management Hefei University of TechnologyHefei230009China School of Statistics Zhejiang Gongshang UniversityHangzhou310018China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of EducationHefei230009China Collaborative Innovation Center for Statistical Data Engineering Technology and Application ZhejiangGongshang UniversityHangzhou310018China

Multiple classifier system exhibits strong classification capacity compared with single classifiers,but they require significant computational *** ensemble system aims to attain equivalent or better classification accuracy with fewer ***,current methods fail to identify precise solutions for constructing an ensemble *** this study,we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm(ECDPB).Considering that extreme learning machines(ELMs)have rapid learning rates and good generalization ability,they can serve as the basic classifier for creating multiple candidates while using fewer computational ***,we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account;it is used to identify the ELMs that have good diversity and low *** addition,we propose an ECDPB with powerful optimizing ability;it is employed to find the optimal subset of *** selected ELMs can then be used to forman ensemble *** on 10 benchmark datasets have been conducted,and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods.

关键词： Ensemble classifier salp swarmalgorithm diversity measure multiple classifiers system extreme learning machine

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PVitNet:An Effective Approach for Android Malware Detection Using Pyramid Feature Processing and Vision Transformer

PVitNet:An Effective Approach for Android Malware Detection ...

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

作者： Denghui Yang Yifan Ding Hao Zhang Yizhou Li School of Cyber Science and Engineering Sichuan University Chengdu China Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University

This presents a significant challenge for detecting and combating malicious software. Users often grant software permissions unknowingly, exposing their devices to risks such as unauthorized access, file manipulation, and malware propagation. Traditional detection algorithms relying on limited permission-based strategies fall short in addressing this issue. To overcome this, we propose PVitNet (Network based On Pyramid Feature processing and Vision Transformer), an Android malware detection method. PVitNet incorporates pyramid feature processing, attention mechanisms, and an automatic feature extraction tool. By leveraging semantic information from feature pyramid models and learning shared characteristics among similar software, we successfully identify Android malware families. Our experiments on the CICMalDroid 2020 dataset demonstrate the effectiveness of our approach, with a 14.96% increase in accuracy and an F1 score of 98.31%.

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RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer 39

RNTrajRec: Road Network Enhanced Trajectory Recovery with Sp...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Chen, Yuqi Zhang, Hanyuan Sun, Weiwei Zheng, Baihua Fudan University School of Computer Science Shanghai Key Laboratory of Data Science China Shanghai Institute of Intelligent Electronics & Systems China Singapore Management University School of Computing and Information Systems Singapore

ISBN: (纸本)9798350322279

GPS trajectories are the essential foundations for many trajectory-based applications. Most applications require a large number of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints. We study the task of trajectory recovery in this paper as a means to increase the sample rate of low sample trajectories. Most existing works on trajectory recovery follow a sequence-to-sequence diagram, with an encoder to encode a trajectory and a decoder to recover real GPS points in the trajectory. However, these works ignore the topology of road network and only use grid information or raw GPS points as input. Therefore, the encoder model is not able to capture rich spatial information of the GPS points along the trajectory, making the prediction less accurate and less spatial consistent. In this paper, we propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It next develops a spatial-temporal transformer model, namely GPSFormer, to learn rich spatial and temporal features along with a Sub-Graph Generation module to capture the spatial features for each GPS point in the trajectory. It finally forwards the outputs of encoder model to a multi-task decoder model to recover the missing GPS points. Extensive experiments based on three large-scale real-life trajectory datasets confirm the effectiveness of our approach. © 2023 IEEE.

关键词： Recovery

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An identity-based anonymous signcryption for vehicular ad hoc networks 2nd

An identity-based anonymous signcryption for vehicular ad ho...

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2nd International Cognitive Cities Conference, IC3 2019

作者： Lu, Yong Yang, Huijie School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China

ISBN: (纸本)9789811561122

The vehicular ad hoc networks (VANETs) can share information of vehicles’ location, speed and route within a certain range by interconnecting vehicles, roadside units (RSU) and the cloud. As people’s daily traffic tools, vehicles reflect users’ daily routines and other privacy. Therefore, privacy protection is very critical in VANETs. However, in the existing schemes, a user’s temporary identity (TID) is generated by the user himself, which is inconvenient to be managed and can be easily forged. The reputation values of users also need to be considered. In this paper, an identity based signcryption scheme for VANETs is proposed. In the novel scheme, TID is generated by key generation center (KCG) and a personal reputation system is produced. The security analysis of the scheme shows that our scheme is practical. © Springer Nature Singapore Pte Ltd 2020.

关键词： Vehicular ad hoc networks

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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation 38

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmenta...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024 Neural information processing systems foundation. All rights reserved.

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Homomorphic Encryption based stem cell assay data sharing study 4

Homomorphic Encryption based stem cell assay data sharing st...

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4th IEEE International Conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Xiao, Hengjiang Liang, Zhihong Huang, Yuxiang Qin, Mingming Xiong, Fei Liu, Mengxin Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Southwest Forestry University Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data China Southwest Forestry University School of Economics and Management Kunming China

ISBN: (纸本)9798350363586

With the rapid development of the biomedical field, stem cell therapy has received widespread attention as an innovative therapy with great potential. However, privacy protection and secure sharing of stem cell testing data are still major challenges. In this paper, we propose a homomorphic encryption-based data sharing scheme for stem cell testing, which achieves efficient and secure data sharing with data privacy by designing a system architecture covering data acquisition, encryption, transmission, decryption and analysis. Experimental results show that the scheme can not only effectively protect data privacy, but also support efficient data processing with less impact on system performance. The main contributions of this paper include designing and implementing a stem cell detection data sharing system based on homomorphic encryption, proposing a homomorphic encryption algorithm optimised for the high-dimensional characteristics of stem cell data, and experimentally verifying the security and effectiveness of the system. Future research directions include optimising homomorphic encryption algorithms, expanding application scenarios, integrating multiple privacy protection techniques, and optimising system performance. © 2024 IEEE.

关键词： data assimilation

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Exploring Consistent Spatio-Temporal Distortion and Stable 3-D DCT Coefficients for Robust Blind Video Watermarking

Exploring Consistent Spatio-Temporal Distortion and Stable 3...

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

作者： Fei Zhang Hongxia Wang Mingze He Ling Yang School of Cyber Science and Engineering Sichuan University Chengdu China Ministry of Education Key Laboratory of Data Protection and Intelligent Management (Sichuan University) China

With the rapid development of mobile Internet and video applications, robust video watermarking technology has become a focal area of research for protecting and tracking intellectual property rights in digital media. An important characteristic of video is that it has both spatial and temporal properties. Previous studies in video watermarking have primarily focused on either spatial or temporal distortions and did not uniformly consider all types of video distortion, which restricts the robustness of video watermarking. In this paper, a novel robust blind video watermarking is proposed by exploring consistent spatio-temporal distortion and stable 3-D DCT coefficients. Our method achieves stronger robustness by uniformly treating the spatial and temporal distortions of the video. The properties of the stable 3-D DCT coefficients are mathematically proved, which makes the scheme generalizable. Extensive experiments have demonstrated that our method is resistant to video compression (H.264/AVC, H.265/HEVC) attacks, geometric attacks, and temporal domain attacks, and outperforms the current state-of-the-art video watermarking schemes.

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Enhanced Screen Shooting Resilient Document Watermarking

Enhanced Screen Shooting Resilient Document Watermarking

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

作者： Heng Wang Hongxia Wang Xinyi Huang Zhenhao Shi School of Cyber Science and Engineering Sichuan University Chengdu China Ministry of Education Key Laboratory of Data Protection and Intelligent Management (Sichuan University) China

The widespread adoption of smartphones has introduced new challenges to document copyright protection, prompting the emergence of Screen-Shooting Resilient Document Watermarking (SSRDW) technology. In recent years, underpainting-based SSRDW techniques have proven to be highly effective. However, after careful study, we find that existing methods fail to simultaneously meet four essential criteria for SSRDW: high imperceptibility, strong robustness, adaptability to text processing, and high efficiency. In this paper, we introduce an enhanced underpainting-based SSRDW approach capable of satisfying all four requirements. Our approach enhances imperceptibility by employing underpainting embedding methods independent of text content. Additionally, we introduce a fast resynchronization mechanism to improve time efficiency. Furthermore, we propose an enhanced watermark extraction method that enhances robustness and enables watermark retrieval even in scenarios involving text processing. Extensive experimental validation underscores the superior performance of our enhanced SSRDW method.

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